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373 results on '"Maturi, M"'

1. Euclid preparation: Determining the weak lensing mass accuracy and precision for galaxy clusters

Author: Euclid Collaboration, Ingoglia, L., Sereno, M., Farrens, S., Giocoli, C., Baumont, L., Lesci, G. F., Moscardini, L., Murray, C., Vannier, M., Biviano, A., Carbone, C., Covone, G., Despali, G., Maturi, M., Maurogordato, S., Meneghetti, M., Radovich, M., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bellagamba, F., Bender, R., Bernardeau, F., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Faustini, F., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillard, W., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Padilla, C., Paech, K., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Salvignol, J. -C., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Bolzonella, M., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Finelli, F., Gracia-Carpio, J., Matthew, S., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Costanzi, M., Cucciati, O., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ezziati, M., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Brun, A. M. C. Le, Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Pagano, L., Patrizii, L., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Schultheis, M., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Vielzeuf, P.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the level of accuracy and precision of cluster weak-lensing (WL) masses measured with the \Euclid data processing pipeline. We use the DEMNUni-Cov $N$-body simulations to assess how well the WL mass probes the true halo mass, and, then, how well WL masses can be recovered in the presence of measurement uncertainties. We consider different halo mass density models, priors, and mass point estimates. WL mass differs from true mass due to, e.g., the intrinsic ellipticity of sources, correlated or uncorrelated matter and large-scale structure, halo triaxiality and orientation, and merging or irregular morphology. In an ideal scenario without observational or measurement errors, the maximum likelihood estimator is the most accurate, with WL masses biased low by $\langle b_M \rangle = -14.6 \pm 1.7 \, \%$ on average over the full range $M_\text{200c} > 5 \times 10^{13} \, M_\odot$ and $z < 1$. Due to the stabilising effect of the prior, the biweight, mean, and median estimates are more precise. The scatter decreases with increasing mass and informative priors significantly reduce the scatter. Halo mass density profiles with a truncation provide better fits to the lensing signal, while the accuracy and precision are not significantly affected. We further investigate the impact of additional sources of systematic uncertainty on the WL mass, namely the impact of photometric redshift uncertainties and source selection, the expected performance of \Euclid cluster detection algorithms, and the presence of masks. Taken in isolation, we find that the largest effect is induced by non-conservative source selection. This effect can be mostly removed with a robust selection. As a final \Euclid-like test, we combine systematic effects in a realistic observational setting and find results similar to the ideal case, $\langle b_M \rangle = - 15.5 \pm 2.4 \, \%$, under a robust selection.
Published: 2024

2. Euclid preparation. The Cosmic Dawn Survey (DAWN) of the Euclid Deep and Auxiliary Fields

Author: Euclid Collaboration, McPartland, C. J. R., Zalesky, L., Weaver, J. R., Toft, S., Sanders, D. B., Mobasher, B., Suzuki, N., Szapudi, I., Valdes, I., Murphree, G., Chartab, N., Allen, N., Taamoli, S., Eisenhardt, P. R. M., Arnouts, S., Atek, H., Brinchmann, J., Castellano, M., Chary, R., Ortiz, O. Chávez, Cuby, J. -G., Finkelstein, S. L., Goto, T., Gwyn, S., Harikane, Y., Inoue, A. K., McCracken, H. J., Mohr, J. J., Oesch, P. A., Ouchi, M., Oguri, M., Rhodes, J., Rottgering, H. J. A., Sawicki, M., Scaramella, R., Scarlata, C., Silverman, J. D., Stern, D., Teplitz, H. I., Shuntov, M., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Aussel, H., Baccigalupi, C., Baldi, M., Bardelli, S., Bender, R., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., George, K., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Surace, C., Tallada-Crespi, P., Tavagnacco, D., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Di Ferdinando, D., Farinelli, R., Gracia-Carpio, J., Mauri, N., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bethermin, M., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Gregorio, A., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Huertas-Company, M., Ilbert, O., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Musi, P., Walton, Nicholas A., Odier, J., Patrizii, L., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Tao, C., Testera, G., Teyssier, R., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Shankar, F.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set., Comment: 16 pages, 10 figures, submitted to A&A; Updated references; Updated author list
Published: 2024

3. Euclid preparation. Exploring the properties of proto-clusters in the Simulated Euclid Wide Survey

Author: Euclid Collaboration, Böhringer, H., Chon, G., Cucciati, O., Dannerbauer, H., Bolzonella, M., De Lucia, G., Cappi, A., Moscardini, L., Giocoli, C., Castignani, G., Hatch, N. A., Andreon, S., Bañados, E., Ettori, S., Fontanot, F., Gully, H., Hirschmann, M., Maturi, M., Mei, S., Pozzetti, L., Schlenker, T., Spinelli, M., Aghanim, N., Altieri, B., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Finelli, F., Gracia-Carpio, J., Matthew, S., Mauri, N., Pöntinen, M., Porciani, C., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Alvi, S., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Balaguera-Antolinez, A., Ballardini, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Costanzi, M., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gonzalez, A. H., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Walton, Nicholas A., Patrizii, L., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Schultheis, M., Sereno, M., Shankar, F., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Verza, G.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Galaxy proto-clusters are receiving an increased interest since most of the processes shaping the structure of clusters of galaxies and their galaxy population are happening at early stages of their formation. The Euclid Survey will provide a unique opportunity to discover a large number of proto-clusters over a large fraction of the sky (14 500 square degrees). In this paper, we explore the expected observational properties of proto-clusters in the Euclid Wide Survey by means of theoretical models and simulations. We provide an overview of the predicted proto-cluster extent, galaxy density profiles, mass-richness relations, abundance, and sky-filling as a function of redshift. Useful analytical approximations for the functions of these properties are provided. The focus is on the redshift range z= 1.5 to 4. We discuss in particular the density contrast with which proto-clusters can be observed against the background in the galaxy distribution if photometric galaxy redshifts are used as supplied by the ESA Euclid mission together with the ground-based photometric surveys. We show that the obtainable detection significance is sufficient to find large numbers of interesting proto-cluster candidates. For quantitative studies, additional spectroscopic follow-up is required to confirm the proto-clusters and establish their richness., Comment: Submitted to Astronomy and Astrophysics, 24 pages, 28 figures
Published: 2024

4. Euclid preparation. LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling

Author: Euclid Collaboration, Congedo, G., Miller, L., Taylor, A. N., Cross, N., Duncan, C. A. J., Kitching, T., Martinet, N., Matthew, S., Schrabback, T., Tewes, M., Welikala, N., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holliman, M. S., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Guinet, D., Hall, A., Hildebrandt, H., Ilić, S., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Macias-Perez, J., Maggio, G., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schewtschenko, J. A., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., and Vergani, D.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Physics - Data Analysis, Statistics and Probability, Statistics - Computation
Abstract: LensMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies; sampling the posterior distribution of galaxy parameters via Markov Chain Monte Carlo; and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. We quantified the scientific performance through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images; realistic clustering with a mean surface number density of 250 arcmin$^{-2}$ ($I_{\rm E}<29.5$) for galaxies, and 6 arcmin$^{-2}$ ($I_{\rm E}<26$) for stars; and a diffraction-limited chromatic PSF with a full width at half maximum of $0.^{\!\prime\prime}2$ and spatial variation across the field of view. LensMC measured objects with a density of 90 arcmin$^{-2}$ ($I_{\rm E}<26.5$) in 4500 deg$^2$. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed elsewhere). We found measurement multiplicative and additive biases of $m_1=(-3.6\pm0.2)\times10^{-3}$, $m_2=(-4.3\pm0.2)\times10^{-3}$, $c_1=(-1.78\pm0.03)\times10^{-4}$, $c_2=(0.09\pm0.03)\times10^{-4}$; a large detection bias with a multiplicative component of $1.2\times10^{-2}$ and an additive component of $-3\times10^{-4}$; and a measurement PSF leakage of $\alpha_1=(-9\pm3)\times10^{-4}$ and $\alpha_2=(2\pm3)\times10^{-4}$. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies ($-5\times10^{-3}$). Although significant, model bias will be straightforward to calibrate given the weak sensitivity. LensMC is publicly available at https://gitlab.com/gcongedo/LensMC, Comment: Appearing on A&A. 28 pages, 18 figures, and 2 tables
Published: 2024

5. Euclid preparation. XLII. A unified catalogue-level reanalysis of weak lensing by galaxy clusters in five imaging surveys

Author: Euclid Collaboration, Sereno, M., Farrens, S., Ingoglia, L., Lesci, G. F., Baumont, L., Covone, G., Giocoli, C., Marulli, F., La Hera, S. Miranda, Vannier, M., Biviano, A., Maurogordato, S., Moscardini, L., Aghanim, N., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bellagamba, F., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Boucaud, A., Bozzo, E., Cerna, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Israel, H., Mauri, N., Neissner, C., Scottez, V., Tenti, M., Wiesmann, M., Akrami, Y., Allevato, V., Baccigalupi, C., Ballardini, M., Benielli, D., Borgani, S., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Gabarra, L., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Liebing, P., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S. Z., Maturi, M., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sakr, Z., Sánchez, A. G., Schneider, A., Sefusatti, E., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Steinwagner, J., Teyssier, R., Valiviita, J., and Viel, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the Euclid combined cluster and weak-lensing pipeline (COMB-CL). COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five independent, recent, lensing surveys (CHFTLenS, DES~SV1, HSC-SSP~S16a, KiDS~DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the Planck PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with literature results collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO detected clusters in the HSC-SSP XXL North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys, at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If such control continues in Stage-IV, Euclid will be able to measure the weak lensing masses of around 13000 (considering shot noise only) or 3000 (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than 3., Comment: 25 pages; in press on A&A
Published: 2024

6. Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Author: Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Zinchenko, I. A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, published in A&A
Published: 2024
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7. The miniJPAS survey: Optical detection of galaxy clusters with PZWav

Author: Doubrawa, L., Cypriano, E. S., Finoguenov, A., Lopes, P. A. A., Gonzalez, A. H., Maturi, M., Dupke, R. A., Delgado, R. M. González, Abramo, R., Benitez, N., Bonoli, S., Carneiro, S., Cenarro, J., Cristóbal-Hornillos, D., Ederoclite, A., Hernán-Caballero, A., López-Sanjuan, C., Marín-Franch, A., de Oliveira, C. Mendes, Moles, M., Sodré Jr., L., Taylor, K., Varela, J., and Ramió, H. Vázquez
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Galaxy clusters are an essential tool to understand and constrain the cosmological parameters of our Universe. Thanks to its multi-band design, J-PAS offers a unique group and cluster detection window using precise photometric redshifts and sufficient depths. We produce galaxy cluster catalogues from the miniJPAS, which is a pathfinder survey for the wider J-PAS survey, using the PZWav algorithm. Relying only on photometric information, we provide optical mass tracers for the identified clusters, including richness, optical luminosity, and stellar mass. By reanalysing the Chandra mosaic of the AEGIS field, alongside the overlapping XMM-Newton observations, we produce an X-ray catalogue. The analysis reveals the possible presence of structures with masses of 4$\times 10^{13}$ M$_\odot$ at redshift 0.75, highlighting the depth of the survey. Comparing results with those from two other cluster catalogues, provided by AMICO and VT, we find $43$ common clusters with cluster centre offsets of 100$\pm$60 kpc and redshift differences below 0.001. We provide a comparison of the cluster catalogues with a catalogue of massive galaxies and report on the significance of cluster selection. In general, we are able to recover approximately 75$\%$ of the galaxies with $M^{\star} >$2$\times 10^{11}$ M$_\odot$. This study emphasises the potential of the J-PAS survey and the employed techniques down to the group scales., Comment: 15 pages, 11 figures, 5 tables. Submitted to A&A in December 19, 2023
Published: 2023

8. Euclid Preparation. XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Author: Euclid Collaboration, Lesci, G. F., Sereno, M., Radovich, M., Castignani, G., Bisigello, L., Marulli, F., Moscardini, L., Baumont, L., Covone, G., Farrens, S., Giocoli, C., Ingoglia, L., La Hera, S. Miranda, Vannier, M., Biviano, A., Maurogordato, S., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Massey, R., Medinaceli, E., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Munari, E., Nakajima, R., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Bolzonella, M., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bruton, S., Burigana, C., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Cucciati, O., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Gwyn, S., Hildebrandt, H., Huertas-Company, M., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Schultheis, M., Sefusatti, E., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., and Vergani, D.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. We calibrated galaxy selections using simulated ground-based $griz$ and Euclid $Y_{\rm E}J_{\rm E}H_{\rm E}$ photometry. Both selections produce a purity higher than 97%. The $griz$ selection completeness ranges from 30% to 84% in the lens redshift range $z_{\rm l}\in[0.2,0.8]$. With the full $grizY_{\rm E}J_{\rm E}H_{\rm E}$ selection, the completeness improves by up to $25$ percentage points, and the $z_{\rm l}$ range extends up to $z_{\rm l}=1.5$. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on $griz$ bands provides excellent results on real external datasets. The $griz$ selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percent points higher completeness. The combination of colour and photo-$z$ selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high $z_{\rm l}$. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-based data. Finally, we show that colour selections do not disrupt the shear calibration for stage III surveys., Comment: 20 pages, 13 figures. Published by A&A
Published: 2023

9. Euclid preparation. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-$\Lambda$CDM models

Author: Euclid Collaboration, Bose, B., Carrilho, P., Marinucci, M., Moretti, C., Pietroni, M., Carella, E., Piga, L., Wright, B. S., Vernizzi, F., Carbone, C., Casas, S., D'Amico, G., Frusciante, N., Koyama, K., Pace, F., Pourtsidou, A., Baldi, M., de la Bella, L. F., Fiorini, B., Giocoli, C., Lombriser, L., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Cardone, V. F., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Costille, A., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hormuth, F., Hornstrup, A., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Seiffert, M., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bernardeau, F., Borgani, S., Bruton, S., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferreira, P. G., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Guinet, D., Hall, A., Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Silvestri, A., Mancini, A. Spurio, Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., and Vergani, D.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in beyond-$\Lambda$CDM scenarios. We examine the normal branch of the Dvali-Gabadadze-Porrati model, the Hu-Sawicki $f(R)$ model, a slowly evolving dark energy, an interacting dark energy model and massive neutrinos. For each, we test approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein-de Sitter universe; we explore different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos; we employ two models for redshift space distortions, the Taruya-Nishimishi-Saito prescription and the Effective Field Theory of Large-Scale Structure. This work further provides a preliminary validation of the codes being considered by Euclid for the spectroscopic clustering probe in beyond-$\Lambda$CDM scenarios. We calculate and compare the $\chi^2$ statistic to assess the different modelling choices. This is done by fitting the spectroscopic clustering predictions to measurements from numerical simulations and perturbation theory-based mock data. We compare the behaviour of this statistic in the beyond-$\Lambda$CDM cases, as a function of the maximum scale included in the fit, to the baseline $\Lambda$CDM case. We find that the Einstein-de Sitter approximation without screening is surprisingly accurate for all cases when comparing to the halo clustering monopole and quadrupole obtained from simulations. Our results suggest that the inclusion of multiple redshift bins, higher-order multipoles, higher-order clustering statistics (such as the bispectrum) and photometric probes such as weak lensing, will be essential to extract information on massive neutrinos, modified gravity and dark energy., Comment: 29 pages, 10 figures, 4 tables, 3 appendices. Journal accepted version
Published: 2023
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10. Euclid preparation. Spectroscopy of active galactic nuclei with NISP

Author: Euclid Collaboration, Lusso, E., Fotopoulou, S., Selwood, M., Allevato, V., Calderone, G., Mancini, C., Mignoli, M., Scodeggio, M., Bisigello, L., Feltre, A., Ricci, F., La Franca, F., Vergani, D., Gabarra, L., Brun, V. Le, Maiorano, E., Palazzi, E., Moresco, M., Zamorani, G., Cresci, G., Jahnke, K., Humphrey, A., Landt, H., Mannucci, F., Marconi, A., Pozzetti, L., Salucci, P., Salvato, M., Shankar, F., Spinoglio, L., Stern, D., Serjeant, S., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auphan, T., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Kümmel, M., Keihänen, E., Kermiche, S., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Merlin, E., Meylan, G., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Vibert, D., Wang, Y., Weller, J., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Mainetti, G., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Bethermin, M., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Guinet, D., Hall, A., Hildebrandt, H., Muñoz, A. Jiminez, Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., and Zinchenko, I. A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The statistical distribution and evolution of key properties (e.g. accretion rate, mass, or spin) of active galactic nuclei (AGN), remain an open debate in astrophysics. The ESA Euclid space mission, launched on July 1st 2023, promises a breakthrough in this field. We create detailed mock catalogues of AGN spectra, from the rest-frame near-infrared down to the ultraviolet, including emission lines, to simulate what Euclid will observe for both obscured (type 2) and unobscured (type 1) AGN. We concentrate on the red grisms of the NISP instrument, which will be used for the wide-field survey, opening a new window for spectroscopic AGN studies in the near-infrared. We quantify the efficiency in the redshift determination as well as in retrieving the emission line flux of the H$\alpha$+[NII] complex as Euclid is mainly focused on this emission line as it is expected to be the brightest one in the probed redshift range. Spectroscopic redshifts are measured for 83% of the simulated AGN in the interval where the H$\alpha$+[NII] is visible (0.892x10^{-16}$ erg s$^{-1}$ cm$^{-2}$, encompassing the peak of AGN activity at $z\simeq 1-1.5$) within the spectral coverage of the red grism. Outside this redshift range, the measurement efficiency decreases significantly. Overall, a spectroscopic redshift is correctly determined for ~90% of type 2 AGN down to an emission line flux of $3x10^{-16}$ erg s$^{-1}$ cm$^{-2}$, and for type 1 AGN down to $8.5x10^{-16}$ erg s$^{-1}$ cm$^{-2}$. Recovered black hole mass values show a small offset with respect to the input values ~10%, but the agreement is good overall. With such a high spectroscopic coverage at z<2, we will be able to measure AGN demography, scaling relations, and clustering from the epoch of the peak of AGN activity down to the present-day Universe for hundreds of thousand AGN with homogeneous spectroscopic information., Comment: 29 pages, 23 figures. Submitted to A&A, revised version
Published: 2023

11. Euclid Preparation. TBD. Impact of magnification on spectroscopic galaxy clustering

Author: Euclid Collaboration, Jelic-Cizmek, G., Sorrenti, F., Lepori, F., Bonvin, C., Camera, S., Castander, F. J., Durrer, R., Fosalba, P., Kunz, M., Lombriser, L., Tutusaus, I., Viglione, C., Sakr, Z., Aghanim, N., Amara, A., Andreon, S., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kurki-Suonio, H., Lilje, P. B., Lindholm, V., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Merlin, E., Meylan, G., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Liebing, P., Mauri, N., Neissner, C., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bruton, S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., {n}as-Herrera, G. Ca\, Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferreira, P. G., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Guinet, D., Hildebrandt, H., Ilić, S., {n}oz, A. Jimenez Mu\, Joudaki, S., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Schultheis, M., Sefusatti, E., Sereno, M., Silvestri, A., Simon, P., Mancini, A. Spurio, Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., Vergani, D., and Tanidis, K.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In this paper we investigate the impact of lensing magnification on the analysis of Euclid's spectroscopic survey, using the multipoles of the 2-point correlation function for galaxy clustering. We determine the impact of lensing magnification on cosmological constraints, and the expected shift in the best-fit parameters if magnification is ignored. We consider two cosmological analyses: i) a full-shape analysis based on the $\Lambda$CDM model and its extension $w_0w_a$CDM and ii) a model-independent analysis that measures the growth rate of structure in each redshift bin. We adopt two complementary approaches in our forecast: the Fisher matrix formalism and the Markov chain Monte Carlo method. The fiducial values of the local count slope (or magnification bias), which regulates the amplitude of the lensing magnification, have been estimated from the Euclid Flagship simulations. We use linear perturbation theory and model the 2-point correlation function with the public code coffe. For a $\Lambda$CDM model, we find that the estimation of cosmological parameters is biased at the level of 0.4-0.7 standard deviations, while for a $w_0w_a$CDM dynamical dark energy model, lensing magnification has a somewhat smaller impact, with shifts below 0.5 standard deviations. In a model-independent analysis aiming to measure the growth rate of structure, we find that the estimation of the growth rate is biased by up to $1.2$ standard deviations in the highest redshift bin. As a result, lensing magnification cannot be neglected in the spectroscopic survey, especially if we want to determine the growth factor, one of the most promising ways to test general relativity with Euclid. We also find that, by including lensing magnification with a simple template, this shift can be almost entirely eliminated with minimal computational overhead., Comment: 23 pages, 14 figures
Published: 2023

12. Euclid preparation. XXXIX. The effect of baryons on the Halo Mass Function

Author: Euclid Collaboration, Castro, T., Borgani, S., Costanzi, M., Dakin, J., Dolag, K., Fumagalli, A., Ragagnin, A., Saro, A., Brun, A. M. C. Le, Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bozzo, E., Cerna, C., Colodro-Conde, C., Di Ferdinando, D., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Borlaff, A. S., Bruton, S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Cucciati, O., Díaz-Sánchez, A., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., Garcia-Bellido, J., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Ilić, S., Munñoz, A. Jimanez, Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schaye, J., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., and Vergani, D.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being sub-dominant to dark matter and dark energy, the baryonic component in our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model to precisely quantify the impact of baryons on galaxy cluster virial halo masses, using the baryon fraction within a cluster as proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters calibrated using non-radiative cosmological hydrodynamical simulations and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable one percent relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters, starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against varying the numerical implementation of the sub-resolution physical processes included in the simulations. Our work substantiates previous claims about the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model, arising from baryonic corrections to cluster masses, are sub-dominant when compared to the precision with which mass-observable relations will be calibrated using Euclid, as well as our current understanding of the baryon fraction within galaxy clusters., Comment: 18 pages, 10 figures, 4 tables, 1 appendix, abstract abridged for arXiv submission; v2 matches published version
Published: 2023

13. AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from angular power spectrum and correlation function

Author: Romanello, M., Marulli, F., Moscardini, L., Lesci, G. F., Sartoris, B., Contarini, S., Giocoli, C., Bardelli, S., Busillo, V., Castignani, G., Covone, G., Ingoglia, L., Maturi, M., Puddu, E., Radovich, M., Roncarelli, M., and Sereno, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We study the tomographic clustering properties of the photometric cluster catalogue derived from the Third Data Release of the Kilo Degree Survey, focusing on the angular correlation function and its spherical harmonic counterpart, the angular power spectrum. We measure the angular correlation function and power spectrum from a sample of 5162 clusters, with an intrinsic richness $\lambda^*\geq 15$, in the photometric redshift range $z\in [0.1, 0.6]$, comparing our measurements with theoretical models, in the framework of the $\Lambda$-Cold Dark Matter cosmology. We perform a Monte Carlo Markov Chain analysis to constrain the cosmological parameters $\Omega_{\mathrm{m}}$, $\sigma_8$ and the structure growth parameter $S_8\equiv\sigma_8 \sqrt{\Omega_{\mathrm{m}}/0.3}$. We adopt Gaussian priors on the parameters of the mass-richness relation, based on the posterior distributions derived from a previous joint analysis of cluster counts and weak lensing mass measurements carried out with the same catalogue. From the angular correlation function, we obtain $\Omega_{\mathrm{m}}=0.32^{+0.05}_{-0.04}$, $\sigma_8=0.77^{+0.13}_{-0.09}$ and $S_8=0.80^{+0.08}_{-0.06}$, in agreement, within $1\sigma$, with 3D clustering result based on the same cluster sample and with existing complementary studies on other datasets. For the angular power spectrum, we derive statistically consistent results, in particular $\Omega_{\mathrm{m}}=0.24^{+0.05}_{-0.04}$ and $S_8=0.93^{+0.11}_{-0.12}$, while the constraint on $\sigma_8$ alone is weaker with respect to the one provided by the angular correlation function, $\sigma_8=1.01^{+0.25}_{-0.17}$. Our results show that the 2D clustering from photometric cluster surveys can provide competitive cosmological constraints with respect to the full 3D clustering statistics, and can be successfully applied to ongoing and forthcoming spectro/photometric surveys., Comment: 14 pages, 9 figures. Submitted to Astronomy & Astrophysics (A&A)
Published: 2023
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14. The miniJPAS survey: clusters and galaxy groups detection with AMICO

Author: Maturi, M., Finoguenov, A., Lopes, P. A. A., Delgado, R. M. González, Dupke, R. A., Cypriano, E. S., Carrasco, E. R., Diego, J. M., Penna-Lima, M., Vílchez, J. M., Moscardini, L., Marra, V., Bonoli, S., Rodríguez-Martín, J. E., Zitrin, A., Márquez, I., Hernán-Caballero, A., Jiménez-Teja, Y., Abramo, R., Alcaniz, J., Benitez, N., Carneiro, S., Cenarro, J., Cristóbal-Hornillos, D., Ederoclite, A., López-Sanjuan, C., Marín-Franch, A., de Oliveira, C. Mendes, Moles, M., Sodré Jr., L., Taylor, K., Varela, J., Ramió, H. Vázquez, and Fernández-Ontiveros, J. A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Samples of galaxy clusters allow us to better understand the physics at play in galaxy formation and to constrain cosmological models once their mass, position (for clustering studies) and redshift are known. In this context, large optical data sets play a crucial role. We investigate the capabilities of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) in detecting and characterizing galaxy groups and clusters. We analyze the data of the miniJPAS survey, obtained with the JPAS-Pathfinder camera and covering $1$ deg$^2$ centered on the AEGIS field to the same depths and with the same 54 narrow band plus 2 broader band near-UV and near-IR filters anticipated for the full J-PAS survey. We use the Adaptive Matched Identifier of Clustered Objects (AMICO) to detect and characterize groups and clusters of galaxies down to $S/N=2.5$ in the redshift range $0.05
Published: 2023
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15. Euclid preparation. XXIX. Water ice in spacecraft part I: The physics of ice formation and contamination

Author: Euclid Collaboration, Schirmer, M., Thürmer, K., Bras, B., Cropper, M., Martin-Fleitas, J., Goueffon, Y., Kohley, R., Mora, A., Portaluppi, M., Racca, G. D., Short, A. D., Szmolka, S., Venancio, L. M. Gaspar, Altmann, M., Balog, Z., Bastian, U., Biermann, M., Busonero, D., Fabricius, C., Grupp, F., Jordi, C., Löffler, W., Sellés, A. Sagristà, Aghanim, N., Amara, A., Amendola, L., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Battaglia, P., Bozzo, E., Colodro-Conde, C., Farina, M., Graciá-Carpio, J., Keihänen, E., Lindholm, V., Maino, D., Mauri, N., Morisset, N., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Ballardini, M., Biviano, A., Blanchard, A., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Courtois, H. M., Cuby, J. -G., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Gabarra, L., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Liebing, P., Loureiro, A., Maggio, G., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Pollack, J. E., Popa, V., Potter, D., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schneider, A., Sereno, M., Shulevski, A., Simon, P., Steinwagner, J., Teyssier, R., and Valiviita, J.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Molecular contamination is a well-known problem in space flight. Water is the most common contaminant and alters numerous properties of a cryogenic optical system. Too much ice means that Euclid's calibration requirements and science goals cannot be met. Euclid must then be thermally decontaminated, a long and risky process. We need to understand how iced optics affect the data and when a decontamination is required. This is essential to build adequate calibration and survey plans, yet a comprehensive analysis in the context of an astrophysical space survey has not been done before. In this paper we look at other spacecraft with well-documented outgassing records, and we review the formation of thin ice films. A mix of amorphous and crystalline ices is expected for Euclid. Their surface topography depends on the competing energetic needs of the substrate-water and the water-water interfaces, and is hard to predict with current theories. We illustrate that with scanning-tunnelling and atomic-force microscope images. Industrial tools exist to estimate contamination, and we must understand their uncertainties. We find considerable knowledge errors on the diffusion and sublimation coefficients, limiting the accuracy of these tools. We developed a water transport model to compute contamination rates in Euclid, and find general agreement with industry estimates. Tests of the Euclid flight hardware in space simulators did not pick up contamination signals; our in-flight calibrations observations will be much more sensitive. We must understand the link between the amount of ice on the optics and its effect on Euclid's data. Little research is available about this link, possibly because other spacecraft can decontaminate easily, quenching the need for a deeper understanding. In our second paper we quantify the various effects of iced optics on spectrophotometric data., Comment: 35 pages, 22 figures, A&A in press. Changes to previous version: language edits, added Z. Bolag as author in the arxiv PDF (was listed in the ASCII author list and in the journal PDF, but not in the arxiv PDF). This version is identical to the journal version
Published: 2023
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16. Star forming brightest cluster galaxies at $z\sim0.4$ in KiDS. Further studies of cold gas and stellar properties

Author: Castignani, G., Radovich, M., Combes, F., Salomé, P., Moscardini, L., Bardelli, S., Giocoli, C., Lesci, G., Marulli, F., Maturi, M., Puddu, E., Sereno, M., and Tramonte, D.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Brightest cluster galaxies (BCGs) are among the most massive galaxies in the Universe. Their star formation (SF) history and stellar mass assembly are debated. Recent studies suggest the presence of an emerging population of intermediate-$z$ star forming and gas-rich BCGs, where the molecular gas reservoirs are impacted by strong environmental processing. We have selected three among the most star-forming $z\sim0.4$ BCGs in the Kilo-Degree Survey (KiDS), and observed them with the IRAM 30m telescope in the first three CO transitions. We found double-horn CO(1$\rightarrow$0) and CO(3$\rightarrow$2) emission for the KiDS 1433 BCG, yielding a large molecular gas reservoir with $M_{H_2}=(5.9\pm1.2)\times10^{10}~M_\odot$ and a high gas-to-stellar mass ratio $M_{H_2}/M_\star=(0.32^{+0.12}_{-0.10})$. We increase the limited sample of distant BCGs with detections in multiple CO transitions. The double-horn emission for the KiDS 1433 BCG implies a low gas concentration, while a modeling of the spectra yields an extended molecular gas reservoir, with a characteristic radius of $\sim$(5-7) kpc, which is reminiscent of a mature extended-disk phase observed in some local BCGs. For the other two BCGs we are able to set upper limits of $M_{H_2}/M_\star<0.07$ and $<0.23$, which are among the lowest for distant BCGs. We then combined our observations with available stellar, SF, and dust properties of the targeted BCGs, and compared them with $\sim100$ distant cluster galaxies, including additional intermediate-$z$ BCGs, with observations in CO from the literature. The molecular gas properties of star forming BCGs are heterogeneous. On one side, gas-rich BCGs show extended gas reservoirs, which sustain the significant SF activity, which is reminiscent of recent gas infall. Conversely, the existence of similarly star forming, but gas-poor, BCGs suggest that gas depletion precedes SF quenching., Comment: 15 pages, 8 figures, version after the proof corrections, A&A in press. arXiv admin note: substantial text overlap with arXiv:2207.12073
Published: 2023
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17. Euclid preparation. XXVII. Covariance model validation for the 2-point correlation function of galaxy clusters

Author: Euclid Collaboration, Fumagalli, A., Saro, A., Borgani, S., Castro, T., Costanzi, M., Monaco, P., Munari, E., Sefusatti, E., Aghanim, N., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Rebolo-Lopez, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Farina, M., Lindholm, V., Maino, D., Mauri, N., Neissner, C., Scottez, V., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Blanchard, A., Borlaff, A. S, Burigana, C., Cabanac, R., Carvalho, C. S., Casas, S., Castignani, G., Chambers, K., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferreira, P. G., Finelli, F., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Muňoz, A. Jimenez, Joachimi, B., Kansal, V., Keihänen, E., Kirkpatrick, C. C., Loureiro, A., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Pollack, J. E., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schirmer, M., Sereno, M., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Valieri, C., Valiviita, J., Veropalumbo, A., and Viel, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, 85A40
Abstract: Aims. We validate a semi-analytical model for the covariance of real-space 2-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the expected Euclid sample of galaxy clusters, we calibrate a simple model to accurately describe the clustering covariance. Then, we use such a model to quantify the likelihood analysis response to variations of the covariance, and investigate the impact of a cosmology-dependent matrix at the level of statistics expected for the Euclid survey of galaxy clusters. Results. We find that a Gaussian model with Poissonian shot-noise does not correctly predict the covariance of the 2-point correlation function of galaxy clusters. By introducing few additional parameters fitted from simulations, the proposed model reproduces the numerical covariance with 10 per cent accuracy, with differences of about 5 per cent on the figure of merit of the cosmological parameters $\Omega_{\rm m}$ and $\sigma_8$. Also, we find that the cosmology-dependence of the covariance adds valuable information that is not contained in the mean value, significantly improving the constraining power of cluster clustering. Finally, we find that the cosmological figure of merit can be further improved by taking mass binning into account. Our results have significant implications for the derivation of cosmological constraints from the 2-point clustering statistics of the Euclid survey of galaxy clusters., Comment: 18 pages, 14 figures
Published: 2022

18. Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning

Author: Euclid Collaboration, Humphrey, A., Bisigello, L., Cunha, P. A. C., Bolzonella, M., Fotopoulou, S., Caputi, K., Tortora, C., Zamorani, G., Papaderos, P., Vergani, D., Brinchmann, J., Moresco, M., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Gomez-Alvarez, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kummel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Farinelli, R., Gracia-Carpio, J., Maino, D., Mauri, N., Mei, S., Morisset, N., Sureau, F., Tenti, M., Tramacere, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Coupon, J., Courtois, H. M., Cucciati, O., Davini, S., De Lucia, G., Dole, H., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Finelli, F., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hook, I., Huertas-Company, M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J. E., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sanchez, A. G., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Viel, M., Calura, F., and Hildebrandt, H.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged), Comment: 37 pages (including appendices), 26 figures; accepted for publication in Astronomy & Astrophysics
Published: 2022
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19. Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies

Author: Euclid Collaboration, Bretonnière, H., Kuchner, U., Huertas-Company, M., Merlin, E., Castellano, M., Tuccillo, D., Buitrago, F., Conselice, C. J., Boucaud, A., Häußler, B., Kümmel, M., Hartley, W. G., Ayllon, A. Alvarez, Bertin, E., Ferrari, F., Ferreira, L., Gavazzi, R., Hernández-Lang, D., Lucatelli, G., Robotham, A. S. G., Schefer, M., Wang, L., Cabanac, R., Sánchez, H. Domínguez, Duc, P. -A., Fotopoulou, S., Kruk, S., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Tortora, C., Aghanim, N., Amara, A., Auricchio, N., Azzollini, R., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pettorino, V., Polenta, G., Poncet, M., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Skottfelt, J., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Mauri, N., Mei, S., Scottez, V., Zucca, E., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Burigana, C., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., De Lucia, G., Desprez, G., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Fontana, A., Ganga, K., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Ilić, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Maturi, M., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valiviita, J., van Mierlo, S. E., Veropalumbo, A., Viel, M., Weaver, J. R., and Scott, D.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper by Merlin et al. focuses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes DeepLeGATo, Galapagos-2, Morfometryka, Profit and SourceXtractor++ on a sample of about 1.5 million simulated galaxies resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic S\'ersic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (10% scatter on ideal S\'ersic simulations) down to an apparent magnitude of about 23 in one component and 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5 respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the Euclid official Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters., Comment: Accepted by A&A. 30 pages, 23+6 figures, Euclid pre-launch key paper. Companion paper: Euclid Collaboration XXV: Merlin et al. 2022 Minor corrections after journal review
Published: 2022
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20. Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

Author: Euclid Collaboration, Merlin, E., Castellano, M., Bretonnière, H., Huertas-Company, M., Kuchner, U., Tuccillo, D., Buitrago, F., Peterson, J. R., Conselice, C. J., Caro, F., Dimauro, P., Nemani, L., Fontana, A., Kümmel, M., Häußler, B., Hartley, W. G., Ayllon, A. Alvarez, Bertin, E., Dubath, P., Ferrari, F., Ferreira, L., Gavazzi, R., Hernández-Lang, D., Lucatelli, G., Robotham, A. S. G., Schefer, M., Tortora, C., Aghanim, N., Amara, A., Amendola, L., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J, Medinaceli, E., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Skottfelt, J., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Mauri, N., Mei, S., Neissner, C., Scottez, V., Tramacere, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A. R., Coupon, J., Courtois, H. M., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Escartin, J. A., Escoffier, S., Farina, M., Ganga, K., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Ilic, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Matthew, S., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., van Mierlo, S. E., Veropalumbo, A., Viel, M., Weaver, J. R., and Scott, D.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut), Comment: 29 pages, 33 figures. Euclid pre-launch key paper. Companion paper: Bretonniere et al. 2022
Published: 2022
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21. Euclid preparation. XXIV. Calibration of the halo mass function in $\Lambda(\nu)$CDM cosmologies

Author: Euclid Collaboration, Castro, T., Fumagalli, A., Angulo, R. E., Bocquet, S., Borgani, S., Carbone, C., Dakin, J., Dolag, K., Giocoli, C., Monaco, P., Ragagnin, A., Saro, A., Sefusatti, E., Costanzi, M., Brun, A. M. C. Le, Corasaniti, P. -S., Amara, A., Amendola, L., Baldi, M., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kermiche, S., Kitching, T., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Seidel, G., Sirri, G., Stanco, L., Crespí, P. Tallada, Taylor, A. N., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Andreon, S., Bardelli, S., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farina, M., Graciá-Carpio, J., Lindholm, V., Neissner, C., Scottez, V., Tenti, M., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Bernardeau, F., Biviano, A., Blanchard, A., Borlaff, A. S., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Davini, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Finelli, F., Ganga, K., Garcia-Bellido, J., George, K., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilić, S., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Marcin, S., Martinelli, M., Martinet, N., Matthew, S., Maturi, M., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schirmer, M., Sereno, M., Mancini, A. Spurio, Teyssier, R., Valiviita, J., Veropalumbo, A., and Viel, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Euclid's photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quantity. We present a new calibration of the analytic HMF, at the level of accuracy and precision required for the uncertainty in this quantity to be subdominant with respect to other sources of uncertainty in recovering cosmological parameters from Euclid cluster counts. Our model is calibrated against a suite of N-body simulations using a Bayesian approach taking into account systematic errors arising from numerical effects in the simulation. First, we test the convergence of HMF predictions from different N-body codes, by using initial conditions generated with different orders of Lagrangian Perturbation theory, and adopting different simulation box sizes and mass resolution. Then, we quantify the effect of using different halo-finder algorithms, and how the resulting differences propagate to the cosmological constraints. In order to trace the violation of universality in the HMF, we also analyse simulations based on initial conditions characterised by scale-free power spectra with different spectral indexes, assuming both Einstein--de Sitter and standard $\Lambda$CDM expansion histories. Based on these results, we construct a fitting function for the HMF that we demonstrate to be sub-percent accurate in reproducing results from 9 different variants of the $\Lambda$CDM model including massive neutrinos cosmologies. The calibration systematic uncertainty is largely sub-dominant with respect to the expected precision of future mass-observation relations; with the only notable exception of the effect due to the halo finder, that could lead to biased cosmological inference., Comment: 25 pages, 21 figures, 5 tables, 3 appendixes; v2 matches published version
Published: 2022
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22. Star forming and gas rich brightest cluster galaxies at $z\sim0.4$ in the Kilo-Degree Survey

Author: Castignani, G., Radovich, M., Combes, F., Salomé, P., Maturi, M., Moscardini, L., Bardelli, S., Giocoli, C., Lesci, G., Marulli, F., Puddu, E., and Sereno, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Brightest Cluster Galaxies (BCGs) are typically massive ellipticals at the centers of clusters. They are believed to experience strong environmental processing, and their mass assembly and star formation history are still debated. We have selected three star forming BCGs in the equatorial field of the Kilo-Degree Survey (KiDS) at intermediate redshifts. We have observed them with the IRAM-30m telescope in the first three CO transitions. We remarkably detected all BCGs at high signal-to-noise ratio ${\rm S/N}\simeq(3.8-10.2)$, for a total of 7 detected lines out of 8, corresponding to a success rate of $88\%$. This allows us to double the number of distant BCGs with clear detections in at least two CO lines. We have then combined our observations with available stellar, star formation, and dust properties of the BCGs, and we have compared them with a sample of $\sim100$ distant cluster galaxies with observations in CO. Our analysis yields large molecular gas reservoirs $M_{H_2}\simeq(0.5-1.4)\times10^{11}~M_\odot$, excitation ratios $r_{31}= L^{\prime}_{\rm CO(3\rightarrow2)}/L^{\prime}_{\rm CO(1\rightarrow0)}\simeq(0.1-0.3)$, long depletion times $\tau_{\rm dep}\simeq(2-4)$~Gyr, and high $M_{H_2}/M_{\rm dust}\simeq(170-300)$. The excitation ratio $r_{31}$ of intermediate-$z$ BCGs appears to be well correlated with the star formation rate and efficiency, which suggests that excited gas is found only in highly star forming and cool-core BCGs. By performing color-magnitude plots and a red sequence modeling we find that recent bursts of star formation are needed to explain the fact that the BCGs are measurably bluer than photometrically selected cluster members. We suggest that a substantial amount of the molecular gas has been accreted by the KiDS BCGs, but still not efficiently converted into stars., Comment: 16 pages, 8 figures, version after the proof corrections, A&A in press
Published: 2022
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23. The miniJPAS survey: The galaxy populations in the most massive cluster in miniJPAS, mJPC2470-1771

Author: Martín, J. E. Rodríguez, Delgado, R. M. González, Martínez-Solaeche, G., Díaz-García, L. A., de Amorim, A., García-Benito, R., Pérez, E., Fernandes, R. Cid, Carrasco, E. R., Maturi, M., Finoguenov, A., Lopes, P. A. A., Cortesi, A., Lucatelli, G., Diego, J. M., Chies-Santos, A. L., Dupke, R. A., Jiménez-Teja, Y., Vílchez, J. M., Abramo, L. R., Alcaniz, J., Benítez, N., Bonoli, S., Cenarro, A. J., Cristóbal-Hornillos, D., Ederoclite, A., Hernán-Caballero, A., López-Sanjuan, C., Marín-Franch, A., de Oliveira, C. Mendes, Moles, M., Sodré Jr., L., Taylor, K., Varela, J., Ramió, H. Vázquez, and Márquez, I.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The miniJPAS is a 1 deg$^2$ survey that uses the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) filter system (54 narrow-band filters) with the Pathfinder camera. We study mJPC2470-1771, the most massive cluster detected in miniJPAS. We study the stellar population properties of the members, their star formation rates (SFR), star formation histories (SFH), the emission line galaxy (ELG) population, their spatial distribution, and the effect of the environment on them, showing the power of J-PAS to study the role of environment in galaxy evolution. We use a spectral energy distribution (SED) fitting code to derive the stellar population properties of the galaxy members: stellar mass, extinction, metallicity, colours, ages, SFH (a delayed-$\tau$ model), and SFRs. Artificial Neural Networks are used for the identification of the ELG population through the detection of H$\alpha$, [NII], H$\beta$, and [OIII] nebular emission. We use the WHAN and BPT diagrams to separate them into star-forming galaxies and AGNs. We find that the fraction of red galaxies increases with the cluster-centric radius. We select 49 ELG, 65.3\% of the them are probably star forming galaxies, and they are dominated by blue galaxies. 24% are likely to host an AGN (Seyfert or LINER galaxies). The rest are difficult to classify and are most likely composite galaxies. Our results are compatible with an scenario where galaxy members were formed roughly at the same epoch, but blue galaxies have had more recent star formation episodes, and they are quenching from inside-out of the cluster centre. The spatial distribution of red galaxies and their properties suggest that they were quenched prior to the cluster accretion or an earlier cluster accretion epoch. AGN feedback and/or mass might also be intervening in the quenching of these galaxies., Comment: 24 pages, 17 figures, 4 tables, accepted in Astronomy & Astrophysics
Published: 2022
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24. The miniJPAS survey: The role of group environment in quenching the star formation

Author: Delgado, R. M. González, Rodríguez-Martín, J. E., Díaz-García, L. A., de Amorim, A., García-Benito, R., Martínez-Solaeche, G., Lopes, P. A. A., Maturi, M., Pérez, E., Fernandes, R. Cid, Cortesi, A., Finoguenov, A., Carrasco, E. R., Hernán-Caballero, A., Abramo, L. R., Alcaniz, J., Benítez, N., Bonoli, S., Cenarro, A. J., Cristóbal-Hornillos, D., Diego, J. M., Dupke, R. A., Ederoclite, A., Fernández-Ontiveros, J. A., López-Sanjuan, C., Marín-Franch, A., Márquez, I., de Oliveira, C. Mendes, Moles, M., Pintos, I., Sodré Jr., L., Taylor, K., Varela, J., Ramió, H. Vázquez, and Vílchez, J. M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The miniJPAS survey has observed $\sim 1$ deg$^2$ on the AEGIS field with 60 bands (spectral resolution of $R \sim 60$) in order to demonstrate the capabilities of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) that will map $\sim 8000$ deg$^2$ of the northern sky in the next years. This paper shows the power of J-PAS to detect low mass groups and characterise their galaxy populations up to $z \sim 1$. We use the spectral energy distribution fitting code BaySeAGal to derive the stellar population properties of the galaxy members in 80 groups at $z \leq 0.8$ previously detected by the AMICO code, as well as for a galaxy field sample retrieved from the whole miniJPAS sample. We identify blue, red, quiescent, and transition galaxy populations through their rest-frame (extinction corrected) colour, stellar mass ($M_\star$) and specific star formation rate. We measure their abundance as a function of $M_\star$ and environment. We find: (i) The fraction of red and quiescent galaxies in groups increases with $M_\star$ and it is always higher in groups than in the field. (ii) The quenched fraction excess (QFE) in groups strongly increases with $M_\star$, (from a few percent to higher than 60% in the mass range $10^{10} - 3 \times 10 ^{11}$ $M_\odot$. (iii) The abundance excess of transition galaxies in groups shows a modest dependence with $M_\star$ (iv) The fading time scale is very short ($<1.5$ Gyr), indicating that the star formation declines very rapidly in groups. (v) The evolution of the galaxy quenching rate in groups shows a modest but significant evolution since $z\sim0.8$, compatible with an evolution with constant $QFE=0.4$, previously measured for satellites in the nearby Universe, and consistent with a scenario where the low-mass star-forming galaxies in clusters at $z= 1-1.4$ are environmentally quenched., Comment: 21 pages, 20 figures, accepted for publication in Astronomy & Astrophysics
Published: 2022
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25. Euclid preparation. XXI. Intermediate-redshift contaminants in the search for $z>6$ galaxies within the Euclid Deep Survey

Author: Euclid Collaboration, van Mierlo, S. E., Caputi, K. I., Ashby, M., Atek, H., Bolzonella, M., Bowler, R. A. A., Brammer, G., Conselice, C. J., Cuby, J., Dayal, P., Díaz-Sánchez, A., Finkelstein, S. L., Hoekstra, H., Humphrey, A., Ilbert, O., McCracken, H. J., Milvang-Jensen, B., Oesch, P. A., Pello, R., Rodighiero, G., Schirmer, M., Toft, S., Weaver, J. R., Wilkins, S. M., Willott, C. J., Zamorani, G., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Graciá-Carpio, J., Maino, D., Mauri, N., Mei, S., Sureau, F., Zucca, E., Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Calura, F., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Coupon, J., Courtois, H. M., Crocce, M., Cucciati, O., Davini, S., Dole, H., Escartin, J. A., Escoffier, S., Fabricius, M., Farina, M., Ganga, K., García-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Gwyn, S., Hook, I., Huertas-Company, M., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nucita, A. A., Patrizii, L., Peel, A., Pollack, J., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sánchez, A. G., Scottez, V., Sefusatti, E., Stadel, J., Teyssier, R., Valiviita, J., and Viel, M.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: (Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$H<27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from the fiducial, 28-band photometry, and fit spectral energy distributions (SEDs) to various combinations of these simulated data. Our study demonstrates that identifying z>6 with Euclid data alone will be very effective, with a z>6 recovery of 91(88)% for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z=1-5.8 contaminants amongst apparent z>6 galaxies as observed with Euclid alone is 18%, which is reduced to 4(13)% by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimized to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-Y)>2.8 and (Y-J)<1.4 colour criteria can separate contaminants from true z>6 galaxies, although these are applicable to only 54% of the contaminants, as many have unconstrained (I-Y) colours. In the most optimistic scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z>6 sample. For the faint mock sample, colour cuts are infeasible., Comment: 27 pages, 14 figures, 9 tables; version accepted for publication by A&A
Published: 2022
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26. AMICO galaxy clusters in KiDS-DR3: Constraints on cosmological parameters and on the normalisation of the mass-richness relation from clustering

Author: Lesci, G. F., Nanni, L., Marulli, F., Moscardini, L., Veropalumbo, A., Maturi, M., Sereno, M., Radovich, M., Bellagamba, F., Roncarelli, M., Bardelli, S., Castignani, G., Covone, G., Giocoli, C., Ingoglia, L., and Puddu, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We analysed the clustering of a photometric sample of galaxy clusters selected from the Third Data Release of the Kilo-Degree Survey, focusing on the redshift-space two-point correlation function (2PCF). We compared our measurements to theoretical predictions of the standard $\Lambda$ cold dark matter ($\Lambda$CDM) cosmological model. We measured the 2PCF of the sample in the cluster centric radial range $r\in[5,80]$ $h^{-1}$Mpc, considering 4934 galaxy clusters with richness $\lambda^*\geq15$ in the redshift range $z\in[0.1,0.6]$. A Markov chain Monte Carlo analysis has been performed to constrain the cosmological parameters $\Omega_{\rm m}$, $\sigma_8$, and $S_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$, assuming Gaussian priors on the mass-richness relation given by the posteriors obtained from a joint analysis of cluster counts and weak lensing. In addition, we constrained the normalisation of the mass-richness relation, $\alpha$, with fixed cosmological parameters. We obtained $\Omega_{\rm m}=0.28^{+0.05}_{-0.04}$, $\sigma_8=0.82^{+0.14}_{-0.12}$, and $S_8=0.80^{+0.08}_{-0.08}$. The constraint on $S_8$ is consistent within 1$\sigma$ with the results from WMAP and Planck. Furthermore, by fixing the cosmological parameters to those provided by Planck, we obtained $\alpha=0.12^{+0.06}_{-0.06}$, which is fully consistent with the result obtained from the joint analysis of cluster counts and weak lensing performed for this sample., Comment: 8 pages, 5 figures, 1 table. Submitted to A&A
Published: 2022
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27. Euclid preparation. XVIII. The NISP photometric system

Author: Euclid Collaboration, Schirmer, M., Jahnke, K., Seidel, G., Aussel, H., Bodendorf, C., Grupp, F., Hormuth, F., Wachter, S., Appleton, P. N., Barbier, R., Brinchmann, J., Carrasco, J. M., Castander, F. J., Coupon, J., De Paolis, F., Franco, A., Ganga, K., Hudelot, P., Jullo, E., Lancon, A., Nucita, A. A., Paltani, S., Smadja, G., Venancio, L. M. G., Strafella, F., Weiler, M., Amara, A., Auphan, T., Auricchio, N., Balestra, A., Bender, R., Bonino, D., Branchini, E., Brescia, M., Capobianco, V., Carbone, C., Carretero, J., Casas, R., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hornstrup, A., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maciaszek, T., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Prieto, E., Raison, F., Rhodes, J., Rix, H. -W., Roncarelli, M., Rossetti, E., Saglia, R., Sartoris, B., Scaramella, R., Schneider, P., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Trifoglio, M., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Boucaud, A., Camera, S., Farinelli, R., Graciá-Carpio, J., Maino, D., Medinaceli, E., Mei, S., Morisset, N., Polenta, G., Renzi, A., Romelli, E., Tenti, M., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Biviano, A., Blanchard, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Cooray, A. R., Courtois, H. M., Crocce, M., Cuby, J. -G., Davini, S., de la Torre, S., Di Ferdinando, D., Escartin, J. A., Farina, M., Ferreira, P. G., Finelli, F., Fotopoulou, S., Galeotta, S., Garcia-Bellido, J., Gaztanaga, E., George, K., Gozaliasl, G., Hook, I. M., Ilić, S., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Mauri, N., McCracken, H. J., Metcalf, R. B., Monaco, P., Morgante, G., Nightingale, J., Patrizii, L., Peel, A., Popa, V., Porciani, C., Potter, D., Reimberg, P., Riccio, G., Sánchez, A. G., Sapone, D., Scottez, V., Sefusatti, E., Teyssier, R., Tutusaus, I., Valieri, C., Valiviita, J., Viel, M., and Hildebrandt, H.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Euclid will be the first space mission to survey most of the extragalactic sky in the 0.95-2.02 $\mu$m range, to a 5$\sigma$ point-source median depth of 24.4 AB mag. This unique photometric data set will find wide use beyond Euclid's core science. In this paper, we present accurate computations of the Euclid Y_E, J_E and H_E passbands used by the Near-Infrared Spectrometer and Photometer (NISP), and the associated photometric system. We pay particular attention to passband variations in the field of view, accounting among others for spatially variable filter transmission, and variations of the angle of incidence on the filter substrate using optical ray tracing. The response curves' cut-on and cut-off wavelengths - and their variation in the field of view - are determined with 0.8 nm accuracy, essential for the photometric redshift accuracy required by Euclid. After computing the photometric zeropoints in the AB mag system, we present linear transformations from and to common ground-based near-infrared photometric systems, for normal stars, red and brown dwarfs, and galaxies separately. A Python tool to compute accurate magnitudes for arbitrary passbands and spectral energy distributions is provided. We discuss various factors from space weathering to material outgassing that may slowly alter Euclid's spectral response. At the absolute flux scale, the Euclid in-flight calibration program connects the NISP photometric system to Hubble Space Telescope spectrophotometric white dwarf standards; at the relative flux scale, the chromatic evolution of the response is tracked at the milli-mag level. In this way, we establish an accurate photometric system that is fully controlled throughout Euclid's lifetime., Comment: 33 pages, 25 figures, accepted for publication in A&A
Published: 2022
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28. Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields

Author: Moneti, Andrea, McCracken, H. J., Shuntov, M., Kauffmann, O. B., Capak, P., Davidzon, I., Ilbert, O., Scarlata, C., Toft, S., Weaver, J., Chary, R., Cuby, J., Faisst, A. L., Masters, D. C., McPartland, C., Mobasher, B., Sanders, D. B., Scaramella, R., Stern, D., Szapudi, I., Teplitz, H., Zalesky, L., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Granett, B. R., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kuemmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespi, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Camera, S., Gracia-Carpio, J., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., Sureau, F., Tenti, M., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Bernardeau, F., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Di Ferdinando, D., Farina, M., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Morisset, N., Nucita, A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Sanchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Teyssier, R., Tubio, O., Tutusaus, I., Valiviita, J., Viel, M., and Hildebrandt, H.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11% of the total Spitzer mission time. The resulting mosaics cover a total of approximately 71.5deg$^2$ in the 3.6 and 4.5um bands, and approximately 21.8deg$^2$ in the 5.8 and 8um bands. They reach at least 24 AB magnitude (measured to sigma, in a 2.5 arcsec aperture) in the 3.6um band and up to ~ 5 mag deeper in the deepest regions. The astrometry is tied to the Gaia astrometric reference system, and the typical astrometric uncertainty for sources with 16<[3.6]<19 is <0.15 arcsec. The photometric calibration is in excellent agreement with previous WISE measurements. We have extracted source number counts from the 3.6um band mosaics and they are in excellent agreement with previous measurements. Given that the Spitzer Space Telescope has now been decommissioned these mosaics are likely to be the definitive reduction of these IRAC data. This survey therefore represents an essential first step in assembling multi-wavelength data on the Euclid deep fields which are set to become some of the premier fields for extragalactic astronomy in the 2020s., Comment: 15 pages with 11 figures, approved by Euclid Consortium Publication Board and submitted to Astronomy and Astrophysics. Data products will become available via the IRSA website once the paper is accepted. This paper is a companion to "COSMOS2020: A panchromatic view of the Universe to z~10 from two complementary catalogs" by John Weaver et al., which is being posted in parallel
Published: 2021
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29. Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering

Author: Lepori, F., Tutusaus, I., Viglione, C., Bonvin, C., Camera, S., Castander, F. J., Durrer, R., Fosalba, P., Jelic-Cizmek, G., Kunz, M., Adamek, J., Casas, S., Martinelli, M., Sakr, Z., Sapone, D., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Ferriol, S., Franceschi, E., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Fabbian, G., Graciá-Carpio, J., Maino, D., Medinaceli, E., Mei, S., Renzi, A., Romelli, E., Sureau, F., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Bernardeau, F., Biviano, A., Blanchard, A., Bolzonella, M., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J. -G., Davini, S., de la Torre, S., Di Ferdinando, D., Farina, M., Ferreira, P. G., Finelli, F., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I. M., Ilić, S., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maoli, R., Martinet, N., Maturi, M., Metcalf, R. B., Monaco, P., Morgante, G., Nightingale, J., Nucita, A., Patrizii, L., Popa, V., Potter, D., Riccio, G., Sánchez, A. G, Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Tramacere, A., Valiviita, J., Viel, M., and Hildebrandt, H.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated specifications, we study the impact of lensing magnification on the constraints and the shift in the estimation of the best fitting cosmological parameters that we expect if this effect is neglected. We follow the prescriptions of the official Euclid Fisher matrix forecast for the photometric galaxy clustering analysis and the combination of photometric clustering and cosmic shear. The slope of the luminosity function (local count slope), which regulates the amplitude of the lensing magnification, and the galaxy bias have been estimated from the Euclid Flagship simulation.We find that magnification significantly affects both the best-fit estimation of cosmological parameters and the constraints in the galaxy clustering analysis of the photometric sample. In particular, including magnification in the analysis reduces the 1$\sigma$ errors on $\Omega_{\text{m},0}, w_{0}, w_a$ at the level of 20-35%, depending on how well we will be able to independently measure the local count slope. In addition, we find that neglecting magnification in the clustering analysis leads to shifts of up to 1.6$\sigma$ in the best-fit parameters. In the joint analysis of galaxy clustering, cosmic shear, and galaxy-galaxy lensing, magnification does not improve precision, but it leads to an up to 6$\sigma$ bias if neglected. Therefore, for all models considered in this work, magnification has to be included in the analysis of galaxy clustering and its cross-correlation with the shear signal ($3\times2$pt analysis) for an accurate parameter estimation., Comment: 26 pages, 11 figures. Version accepted for publication in A&A
Published: 2021
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30. Euclid preparation: XVI. Exploring the ultra low-surface brightness Universe with Euclid/VIS

Author: Borlaff, A. S., Gómez-Alvarez, P., Altieri, B., Marcum, P. M., Vavrek, R., Laureijs, R., Kohley, R., Buitrago, F., Cuillandre, J. C., Duc, P. A., Venancio, L. M. Gaspar, Amara, A., Andreon, S., Auricchio, N., Azzollini, R., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brescia, M., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Candini, G. P., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Ferdinando, D., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Fabricius, M., Farina, M., Farrens, S., Ferreira, P. G., Ferriol, S., Finelli, F., Flose-Reimberg, P., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Ganga, K., Garilli, B., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Keihanen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kirkpatrick, C. C., Kitching, T., Knapen, J. H., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Liebing, P., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Martínez-Delgado, D., Marulli, F., Massey, R., Maturi, M., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Merlin, E., Metcalf, R. B., Meylan, G., Moresco, M., Morgante, G., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. M., Nightingale, J. W., Nucita, A., Padilla, C., Paltani, S., Pasian, F., Patrizii, L., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Sauvage, M., Schneider, P., Scottez, V., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. L., Sureau, F., Tallada-Crespí, P., Taylor, A. N., Tenti, M., Tereno, I., Teyssier, R., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Weller, J., Whittaker, L., Zacchei, A., Zamorani, G., and Zucca, E.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: While Euclid is an ESA mission specifically designed to investigate the nature of Dark Energy and Dark Matter, the planned unprecedented combination of survey area ($\sim15\,000$ deg$^2$), spatial resolution, low sky-background, and depth also make Euclid an excellent space observatory for the study of the low surface brightness Universe. Scientific exploitation of the extended low surface brightness structures requires dedicated calibration procedures yet to be tested. We investigate the capabilities of Euclid to detect extended low surface brightness structure by identifying and quantifying sky background sources and stray-light contamination. We test the feasibility of generating sky flat-fields to reduce large-scale residual gradients in order to reveal the extended emission of galaxies observed in the Euclid Survey. We simulate a realistic set of Euclid/VIS observations, taking into account both instrumental and astronomical sources of contamination, including cosmic rays, stray-light, zodiacal light, ISM, and the CIB, while simulating the effects of the presence of background sources in the FOV. We demonstrate that a combination of calibration lamps, sky flats and self-calibration would enable recovery of emission at a limiting surface brightness magnitude of $\mu=29.5^{+0.08}_{-0.27} $ mag arcsec$^{-2}$ ($3\sigma$, $10\times10$ arcsec$^2$) in the Wide Survey, reaching regions 2 magnitudes deeper in the Deep Surveys. Euclid/VIS has the potential to be an excellent low surface brightness observatory. Covering the gap between pixel-to-pixel calibration lamp flats and self-calibration observations for large scales, the application of sky flat-fielding will enhance the sensitivity of the VIS detector at scales of larger than 1 degree, up to the size of the FOV, enabling Euclid to detect extended surface brightness structures below $\mu=31$ mag arcsec$^{-2}$ and beyond., Comment: 23 pages, 13 figures, Euclid Consortium Key Project, accepted for publication in Astronomy & Astrophysics
Published: 2021
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31. Euclid preparation: I. The Euclid Wide Survey

Author: Scaramella, R., Amiaux, J., Mellier, Y., Burigana, C., Carvalho, C. S., Cuillandre, J. -C., Da Silva, A., Derosa, A., Dinis, J., Maiorano, E., Maris, M., Tereno, I., Laureijs, R., Boenke, T., Buenadicha, G., Dupac, X., Venancio, L. M. Gaspar, Gómez-Álvarez, P., Hoar, J., Alvarez, J. Lorenzo, Racca, G. D., Saavedra-Criado, G., Schwartz, J., Vavrek, R., Schirmer, M., Aussel, H., Azzollini, R., Cardone, V. F., Cropper, M., Ealet, A., Garilli, B., Gillard, W., Granett, B. R., Guzzo, L., Hoekstra, H., Jahnke, K., Kitching, T., Meneghetti, M., Miller, L., Nakajima, R., Niemi, S. M., Pasian, F., Percival, W. J., Sauvage, M., Scodeggio, M., Wachter, S., Zacchei, A., Aghanim, N., Amara, A., Auphan, T., Auricchio, N., Awan, S., Balestra, A., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Candini, G. P., Capobianco, V., Carbone, C., Carlberg, R. G., Carretero, J., Casas, R., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Morin, B., Moscardini, L., Munari, E., Nichol, R. C., Padilla, C., Paltani, S., Peacock, J., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H. -W., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Toledo-Moreo, R., Torradeflot, F., Trifoglio, M., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Welikala, N., Weller, J., Wetzstein, M., Zamorani, G., Zoubian, J., Andreon, S., Baldi, M., Bardelli, S., Boucaud, A., Camera, S., Fabbian, G., Farinelli, R., Graciá-Carpio, J., Maino, D., Medinaceli, E., Mei, S., Neissner, C., Polenta, G., Renzi, A., Romelli, E., Rosset, C., Sureau, F., Tenti, M., Vassallo, T., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Battaglia, P., Biviano, A., Borgani, S., Bozzo, E., Cabanac, R., Cappi, A., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Desai, S., Di Ferdinando, D., Dole, H., Fabricius, M., Farina, M., Ferreira, P. G., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Gozaliasl, G., Hook, I. M., Keihanen, E., Kirkpatrick, C. C., Liebing, P., Lindholm, V., Mainetti, G., Martinelli, M., Martinet, N., Maturi, M., McCracken, H. J., Metcalf, R. B., Morgante, G., Nightingale, J., Nucita, A., Patrizii, L., Potter, D., Riccio, G., Sánchez, A. G., Sapone, D., Schewtschenko, J. A., Schultheis, M., Scottez, V., Teyssier, R., Tutusaus, I., Valiviita, J., Viel, M., Vriend, W., and Whittaker, L.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Euclid is an ESA mission designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging and spectroscopy survey (EWS) in visible and near-infrared, covering roughly 15,000 square degrees of extragalactic sky on six years. The wide-field telescope and instruments are optimized for pristine PSF and reduced straylight, producing very crisp images. This paper presents the building of the Euclid reference survey: the sequence of pointings of EWS, Deep fields, Auxiliary fields for calibrations, and spacecraft movements followed by Euclid as it operates in a step-and-stare mode from its orbit around the Lagrange point L2. Each EWS pointing has four dithered frames; we simulate the dither pattern at pixel level to analyse the effective coverage. We use up-to-date models for the sky background to define the Euclid region-of-interest (RoI). The building of the reference survey is highly constrained from calibration cadences, spacecraft constraints and background levels; synergies with ground-based coverage are also considered. Via purposely-built software optimized to prioritize best sky areas, produce a compact coverage, and ensure thermal stability, we generate a schedule for the Auxiliary and Deep fields observations and schedule the RoI with EWS transit observations. The resulting reference survey RSD_2021A fulfills all constraints and is a good proxy for the final solution. Its wide survey covers 14,500 square degrees. The limiting AB magnitudes ($5\sigma$ point-like source) achieved in its footprint are estimated to be 26.2 (visible) and 24.5 (near-infrared); for spectroscopy, the H$_\alpha$ line flux limit is $2\times 10^{-16}$ erg cm$^{-2}$ s$^{-1}$ at 1600 nm; and for diffuse emission the surface brightness limits are 29.8 (visible) and 28.4 (near-infrared) mag arcsec$^{-2}$., Comment: 43 pages, 51 figures, submitted to A&A
Published: 2021
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32. Euclid Preparation: XIV. The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Data Release 3

Author: Euclid Collaboration, Stanford, S. A., Masters, D., Darvish, B., Stern, D., Cohen, J. G., Capak, P., Hernitschek, N., Davidzon, I., Rhodes, J., Sanders, D. B., Mobasher, B., Castander, F. J., Paltani, S., Aghanim, N., Amara, A., Auricchio, N., Balestra, A., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Casas, R., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Conselice, C. J., Corcione, L., Costille, A., Cropper, M., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kummel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Zamorani, G., Zoubian, J., Brescia, M., Congedo, G., Conversi, L., Copin, Y., Kermiche, S., Kohley, R., Medinaceli, E., Mei, S., Moresco, M., Morin, B., Munari, E., Polenta, G., Sureau, F., Crespí, P. Tallada, Vassallo, T., Zacchei, A., Andreon, S., Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Borsato, E., Bozzo, E., Burigana, C., Cabanac, R., Camera, S., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J. -G., Da Silva, A., de la Torre, S., Di Ferdinando, D., Duncan, C. A. J., Dupac, X., Fabricius, M., Farina, M., Farrens, S., Ferreira, P. G., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Gillard, W., Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Martinet, N., Marulli, F., Maturi, M., Maurogordato, S., Metcalf, R. B., Nakajima, R., Neissner, C., Nightingale, J. W., Nucita, A. A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Romelli, E., Sanchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Stanco, L., Tenti, M., Teyssier, R., Torradeflot, F., Valiviita, J., Viel, M., Whittaker, L., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Complete Calibration of the Color-Redshift Relation (C3R2) survey is obtaining spectroscopic redshifts in order to map the relation between galaxy color and redshift to a depth of i ~ 24.5 (AB). The primary goal is to enable sufficiently accurate photometric redshifts for Stage IV dark energy projects, particularly Euclid and the Roman Space Telescope, which are designed to constrain cosmological parameters through weak lensing. We present 676 new high-confidence spectroscopic redshifts obtained by the C3R2 survey in the 2017B-2019B semesters using the DEIMOS, LRIS, and MOSFIRE multi-object spectrographs on the Keck telescopes. Combined with the 4454 redshifts previously published by this project, the C3R2 survey has now obtained and published 5130 high-quality galaxy spectra and redshifts. If we restrict consideration to only the 0.2 < z(phot) < 2.6 range of interest for the Euclid cosmological goals, then with the current data release C3R2 has increased the spectroscopic redshift coverage of the Euclid color space from 51% (as reported by Masters et al. 2015) to the current 91%. Once completed and combined with extensive data collected by other spectroscopic surveys, C3R2 should provide the spectroscopic calibration set needed to enable photometric redshifts to meet the cosmology requirements for Euclid, and make significant headway toward solving the problem for Roman., Comment: 14 pages, 7 figures, 4 tables. Accepted for publication in The Astrophysical Journal Supplement. Survey website with links to the C3R2 redshift catalog and the spectroscopic data hosted by the Keck Observatory Archive can be found at https://sites.google.com/view/c3r2-survey/home
Published: 2021
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33. $Euclid$ preparation: XV. Forecasting cosmological constraints for the $Euclid$ and CMB joint analysis

Author: Euclid Collaboration, Ilić, S., Aghanim, N., Baccigalupi, C., Bermejo-Climent, J. R., Fabbian, G., Legrand, L., Paoletti, D., Ballardini, M., Archidiacono, M., Douspis, M., Finelli, F., Ganga, K., Hernández-Monteagudo, C., Lattanzi, M., Marinucci, D., Migliaccio, M., Carbone, C., Casas, S., Martinelli, M., Tutusaus, I., Natoli, P., Ntelis, P., Pagano, L., Wenzl, L., Gruppuso, A., Kitching, T., Langer, M., Mauri, N., Patrizii, L., Renzi, A., Sirri, G., Stanco, L., Tenti, M., Vielzeuf, P., Lacasa, F., Polenta, G., Yankelevich, V., Blanchard, A., Sakr, Z., Pourtsidou, A., Camera, S., Cardone, V. F., Kilbinger, M., Kunz, M., Markovic, K., Pettorino, V., Sánchez, A. G., Sapone, D., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Kubik, B., Kümmel, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Starck, J. L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Kleijn, G. A. Verdoes, Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Medinaceli, E., Mei, S., Rosset, C., Sureau, F., Vassallo, T., Zacchei, A., Andreon, S., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Di Ferdinando, D., Dole, H., Farina, M., Ferreira, P. G., Flose-Reimberg, P., Galeotta, S., Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Neissner, C., Nightingale, J., Nucita, A. A., Potter, D., Riccio, G., Romelli, E., Schirmer, M., Schultheis, M., Scottez, V., Teyssier, R., Tramacere, A., Valiviita, J., Viel, M., Whittaker, L., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The combination and cross-correlation of the upcoming $Euclid$ data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of $Euclid$ and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on $Euclid$-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a $Euclid$-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard $\Lambda$-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of $Euclid$ probes with CMB data., Comment: 20 pages, 8 figures, 5 tables, 1 appendix; updated to match version accepted by journal
Published: 2021
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34. Euclid preparation: XI. Mean redshift determination from galaxy redshift probabilities for cosmic shear tomography

Author: Euclid Collaboration, Ilbert, O., de la Torre, S., Martinet, N., Wright, A. H., Paltani, S., Laigle, C., Davidzon, I., Jullo, E., Hildebrandt, H., Masters, D. C., Amara, A., Conselice, C. J., Andreon, S., Auricchio, N., Azzollini, R., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Borgani, S., Boucaud, A., Bozzo, E., Branchini, E., Brescia, M., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Costille, A., Coupon, J., Courtois, H. M., Cropper, M., Cuby, J., Da Silva, A., Degaudenzi, H., Di Ferdinando, D., Dubath, F., Duncan, C., Dupac, X., Dusini, S., Ealet, A., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fotopoulou, S., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Keihanen, E., Kermiche, S., Kiessling, A., Kirkpatrick, C. C., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Metcalf, R. Benton, Moresco, M., Morin, B., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S., Nightingale, J., Padilla, C., Pasian, F., Patrizii, L., Pedersen, K., Pello, R., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sánchez, A. G., Sapone, D., Schneider, P., Schrabback, T., Scottez, V., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Sureau, F., Crespí, P. Tallada, Tenti, M., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tramacere, A., Valentijn, E. A., Valenziano, L., Valiviita, J., Vassallo, T., Wang, Y., Welikala, N., Weller, J., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The analysis of weak gravitational lensing in wide-field imaging surveys is considered to be a major cosmological probe of dark energy. Our capacity to constrain the dark energy equation of state relies on the accurate knowledge of the galaxy mean redshift $\langle z \rangle$. We investigate the possibility of measuring $\langle z \rangle$ with an accuracy better than $0.002\,(1+z)$, in ten tomographic bins spanning the redshift interval $0.299.8\%$. The zPDF approach could also be successful if we debias the zPDF using a spectroscopic training sample. This approach requires deep imaging data, but is weakly sensitive to spectroscopic redshift failures in the training sample. We improve the debiasing method and confirm our finding by applying it to real-world weak-lensing data sets (COSMOS and KiDS+VIKING-450)., Comment: 21 pages, 10 figures, accepted in A&A
Published: 2021
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35. AMICO galaxy clusters in KiDS-DR3: cosmological constraints from counts and stacked weak-lensing

Author: Lesci, G. F., Marulli, F., Moscardini, L., Sereno, M., Veropalumbo, A., Maturi, M., Giocoli, C., Radovich, M., Bellagamba, F., Roncarelli, M., Bardelli, S., Contarini, S., Covone, G., Ingoglia, L., Nanni, L., and Puddu, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a cosmological analysis of abundances and stacked weak-lensing profiles of galaxy clusters, exploiting the AMICO KiDS-DR3 catalogue. The sample consists of 3652 galaxy clusters with intrinsic richness $\lambda^*\geq20$, over an effective area of 377 deg$^2$, in the redshift range $z\in[0.1,\,0.6]$. We quantified the purity and completeness of the sample through simulations. The statistical analysis has been performed by simultaneously modelling the comoving number density of galaxy clusters and the scaling relation between the intrinsic richnesses and the cluster masses, assessed through a stacked weak-lensing profile modelling. The fluctuations of the matter background density, caused by super-survey modes, have been taken into account in the likelihood. Assuming a flat $\Lambda$CDM model, we constrained $\Omega_{\rm m}$, $\sigma_8$, $S_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$, and the parameters of the mass-richness scaling relation. We obtained $\Omega_{\rm m}=0.24^{+0.03}_{-0.04}$, $\sigma_8=0.86^{+0.07}_{-0.07}$, $S_8=0.78^{+0.04}_{-0.04}$. The constraint on $S_8$ is consistent within 1$\sigma$ with the results from WMAP and Planck. Furthermore, we got constraints on the cluster mass scaling relation in agreement with those obtained from a previous weak-lensing only analysis., Comment: 12 pages, 7 figures, 2 tables. Accepted by A&A
Published: 2020
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36. AMICO galaxy clusters in KiDS-DR3: Evolution of the luminosity function between z=0.1 and z=0.8

Author: Puddu, E., Radovich, M., Sereno, M., Bardelli, S., Maturi, M., Moscardini, L., Bellagamba, F., Giocoli, C., Marulli, F., and Roncarelli, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: By means of the $r$-band luminosity function (LF) of galaxies in a sample of about 4000 clusters detected by the cluster finder AMICO in the KiDS-DR3 area of about 400 deg$^2$, we studied the evolution with richness and redshift of the passive evolving (red), star-forming (blue), and total galaxy populations. This analysis was performed for clusters in the redshift range [0.1,0.8] and in the mass range [$10^{13} M_{\odot}$,$10^{15} M_{\odot}$]. To compute LFs, we binned the luminosity distribution in magnitude and statistically subtracted the background. Then, we divided the cluster sample in bins of both redshift and richness/mass. We stacked LF counts in each 2D bin for the total, red, and blue galaxy populations; finally, we fitted the stacked LF with a Schechter function and studied the trend of its parameters with redshift and richness/mass. We found a passive evolution with $z$ for the bright part of the LF for the red and total populations and no significant trends for the faint galaxies. The mass/richness dependence is clear for the density parameter $\Phi_{\star}$, increasing with richness, and for the total population faint end, which is shallower in the rich clusters., Comment: 15 pages, 12 figures, to published in A&A
Published: 2020
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37. Euclid preparation: X. The Euclid photometric-redshift challenge

Author: Euclid Collaboration, Desprez, G., Paltani, S., Coupon, J., Almosallam, I., Alvarez-Ayllon, A., Amaro, V., Brescia, M., Brodwin, M., Cavuoti, S., De Vicente-Albendea, J., Fotopoulou, S., Hatfield, P. W., Hartley, W. G., Ilbert, O., Jarvis, M. J., Longo, G., Saha, R., Speagle, J. S., Tramacere, A., Castellano, M., Dubath, F., Galametz, A., Kuemmel, M., Laigle, C., Merlin, E., Mohr, J. J., Pilo, S., Salvato, M., Rau, M. M., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Bender, R., Biviano, A., Bodendorf, C., Bonino, D., Bozzo, E., Branchini, E., Brinchmann, J., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castander, F. J., Castignani, G., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courtois, H. M., Cuby, J. -G., Da Silva, A., de la Torre, S., Degaudenzi, H., Di Ferdinando, D., Douspis, M., Duncan, C. A. J., Dupac, X., Ealet, A., Fabbian, G., Fabricius, M., Farrens, S., Ferreira, P. G., Finelli, F., Fosalba, P., Fourmanoit, N., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gozaliasl, G., Graciá-Carpio, J., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Holmes, W., Hormuth, F., Humphrey, A., Jahnke, K., Keihanen, E., Kermiche, S., Kilbinger, M., Kirkpatrick, C. C., Kitching, T. D., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Maiorano, E., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maturi, M., Mauri, N., Maurogordato, S., Medinaceli, E., Mei, S., Meneghetti, M., Metcalf, R. Benton, Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S., Padilla, C., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Potter, D., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rossetti, E., Saglia, R., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Stern, D., Sureau, F., Crespí, P. Tallada, Tavagnacco, D., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zamorani, G., Zoubian, J., and Zucca, E.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-$z$s at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2--2.6 redshift range that the Euclid mission will probe. We design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are provided to the participants; and the validation sample, containing only the photometry, to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates sources they consider unfit for use in cosmological analyses. The performance of each method is assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, sources for which the photo-z deviates by more than 0.15(1+z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. [abridged], Comment: Accepted in A&A, 25 pages, 13 figures, 7 tables
Published: 2020
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38. The miniJPAS survey: a preview of the Universe in 56 colours

Author: Bonoli, S., Marín-Franch, A., Varela, J., Ramió, H. Vázquez, Abramo, L. R., Cenarro, A. J., Dupke, R. A., Vílchez, J. M., Cristóbal-Hornillos, D., Delgado, R. M. González, Hernández-Monteagudo, C., López-Sanjuan, C., Muniesa, D. J., Civera, T., Ederoclite, A., Hernán-Caballero, A., Marra, V., Baqui, P. O., Cortesi, A., Cypriano, E. S., Daflon, S., de Amorim, A. L., Díaz-García, L. A., Diego, J. M., Martínez-Solaeche, G., Pérez, E., Placco, V. M., Prada, F., Queiroz, C., Alcaniz, J., Alvarez-Candal, A., Cepa, J., Maroto, A. L., Roig, F., Siffert, B. B., Taylor, K., Benitez, N., Moles, M., Sodré Jr., L., Carneiro, S., de Oliveira, C. Mendes, Abdalla, E., Angulo, R. E., Resco, M. Aparicio, Balaguera-Antolínez, A., Ballesteros, F. J., Brito-Silva, D., Broadhurst, T., Carrasco, E. R., Castro, T., Fernandes, R. Cid, Coelho, P., de Melo, R. B., Doubrawa, L., Fernandez-Soto, A., Ferrari, F., Finoguenov, A., García-Benito, R., Iglesias-Páramo, J., Jiménez-Teja, Y., Kitaura, F. S., Laur, J., Lopes, P. A. A., Lucatelli, G., Martínez, V. J., Maturi, M., Quartin, M., Pigozzo, C., Rodrìguez-Martìn, J. E., Salzano, V., Tamm, A., Tempel, E., Umetsu, K., Valdivielso, L., von Marttens, R., Zitrin, A., Díaz-Martín, M. C., López-Alegre, G., López-Sainz, A., Yanes-Díaz, A., Rueda-Teruel, F., Rueda-Teruel, S., Ibañez, J. Abril, Bravo, J. L Antón, Ferrer, R. Bello, Bielsa, S., Casino, J. M., Castillo, J., Chueca, S., Cuesta, L., Calderaro, J. Garzarán, Iglesias-Marzoa, R., Íniguez, C., Gutierrez, J. L. Lamadrid, Lopez-Martinez, F., Lozano-Pérez, D., Sacristán, N. Maícas, Molina-Ibáñez, E. L., Moreno-Signes, A., Llano, S. Rodríguez, Navarro, M. Royo, Rua, V. Tilve, Andrade, U., Alfaro, E. J., Akras, S., Arnalte-Mur, P., Ascaso, B., Barbosa, C. E., Jiménez, J. Beltrán, Benetti, M., Bengaly, C. A. P., Bernui, A., Blanco-Pillado, J. J., Fernandes, M. Borges, Bregman, J. N., Bruzual, G., Calderone, G., Carvano, J. M., Casarini, L., Chies-Santos, A. L, de Carvalho, G. Coutinho, Dimauro, P., Puertas, S. Duarte, Figueruelo, D., González-Serrano, J. I., Guerrero, M. A., Gurung-López, S., Herranz, D., Huertas-Company, M., Irwin, J. A., Izquierdo-Villalba, D., Kanaan, A., Kehrig, C., Kirkpatrick, C. C., Lim, J., Lopes, A. R., de Oliveira, R. Lopes, Marcos-Caballero, A., Martínez-Delgado, D., Martínez-González, E., Martínez-Somonte, G., Oliveira, N., Orsi, A. A., Overzier, R. A., Penna-Lima, M., Reis, R. R. R., Spinoso, D., Tsujikawa, S., Vielva, P., Vitorelli, A. Z., Xia, J. Q., Yuan, H. B., Arroyo-Polonio, A., Dantas, M. L. L., Galarza, C. A., Gonçalves, D. R., Gonçalves, R. S., Gonzalez, J. E., Gonzalez, A. H., Greisel, N., Landim, R. G., Lazzaro, D., Magris, G., Monteiro-Oliveira, R., Pereira, C. B., Rebouças, M. J., Rodriguez-Espinosa, J. M., da Costa, S. Santos, and Telles, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will soon start to scan thousands of square degrees of the northern extragalactic sky with a unique set of $56$ optical filters from a dedicated $2.55$m telescope, JST, at the Javalambre Astrophysical Observatory. Before the arrival of the final instrument (a 1.2 Gpixels, 4.2deg$^2$ field-of-view camera), the JST was equipped with an interim camera (JPAS-Pathfinder), composed of one CCD with a 0.3deg$^2$ field-of-view and resolution of 0.23 arcsec pixel$^{-1}$. To demonstrate the scientific potential of J-PAS, with the JPAS-Pathfinder camera we carried out a survey on the AEGIS field (along the Extended Groth Strip), dubbed miniJPAS. We observed a total of $\sim 1$ deg$^2$, with the $56$ J-PAS filters, which include $54$ narrow band (NB, $\rm{FWHM} \sim 145$Angstrom) and two broader filters extending to the UV and the near-infrared, complemented by the $u,g,r,i$ SDSS broad band (BB) filters. In this paper we present the miniJPAS data set, the details of the catalogues and data access, and illustrate the scientific potential of our multi-band data. The data surpass the target depths originally planned for J-PAS, reaching $\rm{mag}_{\rm {AB}}$ between $\sim 22$ and $23.5$ for the NB filters and up to $24$ for the BB filters ($5\sigma$ in a $3$~arcsec aperture). The miniJPAS primary catalogue contains more than $64,000$ sources extracted in the $r$ detection band with forced photometry in all other bands. We estimate the catalogue to be complete up to $r=23.6$ for point-like sources and up to $r=22.7$ for extended sources. Photometric redshifts reach subpercent precision for all sources up to $r=22.5$, and a precision of $\sim 0.3$% for about half of the sample. (Abridged), Comment: The miniJPAS data and associated value added catalogues are publicly accessible via this url: http://archive.cefca.es/catalogues/minijpas-pdr201912
Published: 2020
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39. Nature versus nurture: relic nature and environment of the most massive passive galaxies at $z < 0.5$

Author: Tortora, C., Napolitano, N. R., Radovich, M., Spiniello, C., Hunt, L., Roy, N., Moscardini, L., Scognamiglio, D., Spavone, M., Brescia, M., Cavuoti, S., D`Ago, G., Longo, G., Bellagamba, F., Maturi, M., and Roncarelli, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Relic galaxies are thought to be the progenitors of high-redshift red nuggets that for some reason missed the channels of size growth and evolved passively and undisturbed since the first star formation burst (at $z>2$). These local ultracompact old galaxies are unique laboratories for studying the star formation processes at high redshift and thus the early stage of galaxy formation scenarios. Counterintuitively, theoretical and observational studies indicate that relics are more common in denser environments, where merging events predominate. To verify this scenario, we compared the number counts of a sample of ultracompact massive galaxies (UCMGs) selected within the third data release of the Kilo Degree Survey, that is, systems with sizes $R_{\rm e} < 1.5 \, \rm kpc$ and stellar masses $M_{\rm \star} > 8 \times 10^{10}\, \rm M_{\odot}$, with the number counts of galaxies with the same masses but normal sizes in field and cluster environments. Based on their optical and near-infrared colors, these UCMGs are likely to be mainly old, and hence representative of the relic population. We find that both UCMGs and normal-size galaxies are more abundant in clusters and their relative fraction depends only mildly on the global environment, with denser environments penalizing the survival of relics. Hence, UCMGs (and likely relics overall) are not special because of the environment effect on their nurture, but rather they are just a product of the stochasticity of the merging processes regardless of the global environment in which they live., Comment: Accepted for publication on Astronomy & Astrophysics Letters, 6 pages, 1 figure
Published: 2020
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40. Euclid preparation III. Galaxy cluster detection in the wide photometric survey, performance and algorithm selection

Author: Euclid Collaboration, Adam, R., Vannier, M., Maurogordato, S., Biviano, A., Adami, C., Ascaso, B., Bellagamba, F., Benoist, C., Cappi, A., Díaz-Sánchez, A., Durret, F., Farrens, S., Gonzalez, A. H., Iovino, A., Licitra, R., Maturi, M., Mei, S., Merson, A., Munari, E., Pelló, R., Ricci, M., Rocci, P. F., Roncarelli, M., Sarron, F., Amoura, Y., Andreon, S., Apostolakos, N., Arnaud, M., Bardelli, S., Bartlett, J., Baugh, C. M., Borgani, S., Brodwin, M., Castander, F., Castignani, G., Cucciati, O., De Lucia, G., Dubath, P., Fosalba, P., Giocoli, C., Hoekstra, H., Mamon, G. A., Melin, J. B., Moscardini, L., Paltani, S., Radovich, M., Sartoris, B., Schultheis, M., Sereno, M., Weller, J., Burigana, C., Carvalho, C. S., Corcione, L., Kurki-Suonio, H., Lilje, P. B., Sirri, G., Toledo-Moreo, R., and Zamorani, G.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Galaxy cluster counts in bins of mass and redshift have been shown to be a competitive probe to test cosmological models. This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates. The Euclid wide survey will cover 15000 deg$^2$ of the sky in the optical and near-infrared bands, down to magnitude 24 in the $H$-band. The resulting data will make it possible to detect a large number of galaxy clusters spanning a wide-range of masses up to redshift $\sim 2$. This paper presents the final results of the Euclid Cluster Finder Challenge (CFC). The objective of these challenges was to select the cluster detection algorithms that best meet the requirements of the Euclid mission. The final CFC included six independent detection algorithms, based on different techniques, such as photometric redshift tomography, optimal filtering, hierarchical approach, wavelet and friend-of-friends algorithms. These algorithms were blindly applied to a mock galaxy catalog with representative Euclid-like properties. The relative performance of the algorithms was assessed by matching the resulting detections to known clusters in the simulations. Several matching procedures were tested, thus making it possible to estimate the associated systematic effects on completeness to $<3$%. All the tested algorithms are very competitive in terms of performance, with three of them reaching $>80$% completeness for a mean purity of 80% down to masses of $10^{14}$ M$_{\odot}$ and up to redshift $z=2$. Based on these results, two algorithms were selected to be implemented in the Euclid pipeline, the AMICO code, based on matched filtering, and the PZWav code, based on an adaptive wavelet approach. [abridged], Comment: A&A, in press. Only changes in affiliations and names between v1,v2,v3
Published: 2019
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41. Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Author: Lesci, G, Sereno, M, Radovich, M, Castignani, G, Bisigello, L, Marulli, F, Moscardini, L, Baumont, L, Covone, G, Farrens, S, Giocoli, C, Ingoglia, L, Miranda La Hera, S, Vannier, M, Biviano, A, Maurogordato, S, Aghanim, N, Amara, A, Andreon, S, Auricchio, N, Baldi, M, Bardelli, S, Bender, R, Bodendorf, C, Bonino, D, Branchini, E, Brescia, M, Brinchmann, J, Camera, S, Capobianco, V, Carbone, C, Carretero, J, Casas, S, Castander, F, Castellano, M, Cavuoti, S, Cimatti, A, Congedo, G, Conselice, C, Conversi, L, Copin, Y, Corcione, L, Courbin, F, Courtois, H, Da Silva, A, Degaudenzi, H, Di Giorgio, A, Dinis, J, Dubath, F, Duncan, C, Dupac, X, Dusini, S, Farina, M, Ferriol, S, Fosalba, P, Fotopoulou, S, Frailis, M, Franceschi, E, Franzetti, P, Fumana, M, Galeotta, S, Garilli, B, Gillis, B, Grazian, A, Grupp, F, Haugan, S, Hook, I, Hormuth, F, Hornstrup, A, Hudelot, P, Jahnke, K, Kummel, M, Kermiche, S, Kiessling, A, Kilbinger, M, Kubik, B, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, P, Lindholm, V, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Martinet, N, Massey, R, Medinaceli, E, Melchior, M, Mellier, Y, Meneghetti, M, Merlin, E, Meylan, G, Moresco, M, Munari, E, Nakajima, R, Niemi, S, Padilla, C, Paltani, S, Pasian, F, Pedersen, K, Pettorino, V, Pires, S, Polenta, G, Poncet, M, Popa, L, Pozzetti, L, Raison, F, Rebolo, R, Renzi, A, Rhodes, J, Riccio, G, Romelli, E, Roncarelli, M, Rossetti, E, Saglia, R, Sapone, D, Sartoris, B, Schirmer, M, Schneider, P, Secroun, A, Seidel, G, Serrano, S, Sirignano, C, Sirri, G, Skottfelt, J, Stanco, L, Starck, J, Tallada-Crespi, P, Taylor, A, Teplitz, H, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, E, Valenziano, L, Vassallo, T, Veropalumbo, A, Wang, Y, Weller, J, Zacchei, A, Zamorani, G, Zoubian, J, Zucca, E, Bolzonella, M, Bozzo, E, Colodro-Conde, C, Di Ferdinando, D, Gracia-Carpio, J, Marcin, S, Mauri, N, Neissner, C, Nucita, A, Sakr, Z, Scottez, V, Tenti, M, Viel, M, Wiesmann, M, Akrami, Y, Anselmi, S, Baccigalupi, C, Ballardini, M, Borgani, S, Borlaff, A, Bruton, S, Burigana, C, Cabanac, R, Calabro, A, Cappi, A, Carvalho, C, Castro, T, Canas-Herrera, G, Chambers, K, Cooray, A, Coupon, J, Cucciati, O, Davini, S, De La Torre, S, De Lucia, G, Desprez, G, Di Domizio, S, Dole, H, Diaz-Sanchez, A, Escartin Vigo, J, Escoffier, S, Ferrero, I, Finelli, F, Gabarra, L, Ganga, K, Garcia-Bellido, J, Giacomini, F, Gozaliasl, G, Gwyn, S, Hildebrandt, H, Huertas-Company, M, Jimenez Munoz, A, Kajava, J, Kansal, V, Kirkpatrick, C, Legrand, L, Loureiro, A, MacIas-Perez, J, Magliocchetti, M, Mainetti, G, Maoli, R, Martinelli, M, Martins, C, Matthew, S, Maturi, M, Maurin, L, Metcalf, R, Migliaccio, M, Monaco, P, Morgante, G, Nadathur, S, Patrizii, L, Pezzotta, A, Porciani, C, Potter, D, Pontinen, M, Reimberg, P, Rocci, P, Sanchez, A, Schneider, A, Schultheis, M, Sefusatti, E, Simon, P, Spurio Mancini, A, Stanford, S, Steinwagner, J, Testera, G, Teyssier, R, Toft, S, Tosi, S, Troja, A, Tucci, M, Valiviita, J, Vergani, D, Lesci G. F., Sereno M., Radovich M., Castignani G., Bisigello L., Marulli F., Moscardini L., Baumont L., Covone G., Farrens S., Giocoli C., Ingoglia L., Miranda La Hera S., Vannier M., Biviano A., Maurogordato S., Aghanim N., Amara A., Andreon S., Auricchio N., Baldi M., Bardelli S., Bender R., Bodendorf C., Bonino D., Branchini E., Brescia M., Brinchmann J., Camera S., Capobianco V., Carbone C., Carretero J., Casas S., Castander F. J., Castellano M., Cavuoti S., Cimatti A., Congedo G., Conselice C. J., Conversi L., Copin Y., Corcione L., Courbin F., Courtois H. M., Da Silva A., Degaudenzi H., Di Giorgio A. M., Dinis J., Dubath F., Duncan C. A. J., Dupac X., Dusini S., Farina M., Ferriol S., Fosalba P., Fotopoulou S., Frailis M., Franceschi E., Franzetti P., Fumana M., Galeotta S., Garilli B., Gillis B., Grazian A., Grupp F., Haugan S. V. H., Hook I., Hormuth F., Hornstrup A., Hudelot P., Jahnke K., Kummel M., Kermiche S., Kiessling A., Kilbinger M., Kubik B., Kunz M., Kurki-Suonio H., Ligori S., Lilje P. B., Lindholm V., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Martinet N., Massey R., Medinaceli E., Melchior M., Mellier Y., Meneghetti M., Merlin E., Meylan G., Moresco M., Munari E., Nakajima R., Niemi S. -M., Padilla C., Paltani S., Pasian F., Pedersen K., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Pozzetti L., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Rossetti E., Saglia R., Sapone D., Sartoris B., Schirmer M., Schneider P., Secroun A., Seidel G., Serrano S., Sirignano C., Sirri G., Skottfelt J., Stanco L., Starck J. -L., Tallada-Crespi P., Taylor A. N., Teplitz H. I., Tereno I., Toledo-Moreo R., Torradeflot F., Tutusaus I., Valentijn E. A., Valenziano L., Vassallo T., Veropalumbo A., Wang Y., Weller J., Zacchei A., Zamorani G., Zoubian J., Zucca E., Bolzonella M., Bozzo E., Colodro-Conde C., Di Ferdinando D., Gracia-Carpio J., Marcin S., Mauri N., Neissner C., Nucita A. A., Sakr Z., Scottez V., Tenti M., Viel M., Wiesmann M., Akrami Y., Anselmi S., Baccigalupi C., Ballardini M., Borgani S., Borlaff A. S., Bruton S., Burigana C., Cabanac R., Calabro A., Cappi A., Carvalho C. S., Castro T., Canas-Herrera G., Chambers K. C., Cooray A. R., Coupon J., Cucciati O., Davini S., De La Torre S., De Lucia G., Desprez G., Di Domizio S., Dole H., Diaz-Sanchez A., Escartin Vigo J. A., Escoffier S., Ferrero I., Finelli F., Gabarra L., Ganga K., Garcia-Bellido J., Giacomini F., Gozaliasl G., Gwyn S., Hildebrandt H., Huertas-Company M., Jimenez Munoz A., Kajava J. J. E., Kansal V., Kirkpatrick C. C., Legrand L., Loureiro A., MacIas-Perez J., Magliocchetti M., Mainetti G., Maoli R., Martinelli M., Martins C. J. A. P., Matthew S., Maturi M., Maurin L., Metcalf R. B., Migliaccio M., Monaco P., Morgante G., Nadathur S., Patrizii L., Pezzotta A., Porciani C., Potter D., Pontinen M., Reimberg P., Rocci P. -F., Sanchez A. G., Schneider A., Schultheis M., Sefusatti E., Simon P., Spurio Mancini A., Stanford S. A., Steinwagner J., Testera G., Teyssier R., Toft S., Tosi S., Troja A., Tucci M., Valiviita J., Vergani D., Lesci, G, Sereno, M, Radovich, M, Castignani, G, Bisigello, L, Marulli, F, Moscardini, L, Baumont, L, Covone, G, Farrens, S, Giocoli, C, Ingoglia, L, Miranda La Hera, S, Vannier, M, Biviano, A, Maurogordato, S, Aghanim, N, Amara, A, Andreon, S, Auricchio, N, Baldi, M, Bardelli, S, Bender, R, Bodendorf, C, Bonino, D, Branchini, E, Brescia, M, Brinchmann, J, Camera, S, Capobianco, V, Carbone, C, Carretero, J, Casas, S, Castander, F, Castellano, M, Cavuoti, S, Cimatti, A, Congedo, G, Conselice, C, Conversi, L, Copin, Y, Corcione, L, Courbin, F, Courtois, H, Da Silva, A, Degaudenzi, H, Di Giorgio, A, Dinis, J, Dubath, F, Duncan, C, Dupac, X, Dusini, S, Farina, M, Ferriol, S, Fosalba, P, Fotopoulou, S, Frailis, M, Franceschi, E, Franzetti, P, Fumana, M, Galeotta, S, Garilli, B, Gillis, B, Grazian, A, Grupp, F, Haugan, S, Hook, I, Hormuth, F, Hornstrup, A, Hudelot, P, Jahnke, K, Kummel, M, Kermiche, S, Kiessling, A, Kilbinger, M, Kubik, B, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, P, Lindholm, V, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Martinet, N, Massey, R, Medinaceli, E, Melchior, M, Mellier, Y, Meneghetti, M, Merlin, E, Meylan, G, Moresco, M, Munari, E, Nakajima, R, Niemi, S, Padilla, C, Paltani, S, Pasian, F, Pedersen, K, Pettorino, V, Pires, S, Polenta, G, Poncet, M, Popa, L, Pozzetti, L, Raison, F, Rebolo, R, Renzi, A, Rhodes, J, Riccio, G, Romelli, E, Roncarelli, M, Rossetti, E, Saglia, R, Sapone, D, Sartoris, B, Schirmer, M, Schneider, P, Secroun, A, Seidel, G, Serrano, S, Sirignano, C, Sirri, G, Skottfelt, J, Stanco, L, Starck, J, Tallada-Crespi, P, Taylor, A, Teplitz, H, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, E, Valenziano, L, Vassallo, T, Veropalumbo, A, Wang, Y, Weller, J, Zacchei, A, Zamorani, G, Zoubian, J, Zucca, E, Bolzonella, M, Bozzo, E, Colodro-Conde, C, Di Ferdinando, D, Gracia-Carpio, J, Marcin, S, Mauri, N, Neissner, C, Nucita, A, Sakr, Z, Scottez, V, Tenti, M, Viel, M, Wiesmann, M, Akrami, Y, Anselmi, S, Baccigalupi, C, Ballardini, M, Borgani, S, Borlaff, A, Bruton, S, Burigana, C, Cabanac, R, Calabro, A, Cappi, A, Carvalho, C, Castro, T, Canas-Herrera, G, Chambers, K, Cooray, A, Coupon, J, Cucciati, O, Davini, S, De La Torre, S, De Lucia, G, Desprez, G, Di Domizio, S, Dole, H, Diaz-Sanchez, A, Escartin Vigo, J, Escoffier, S, Ferrero, I, Finelli, F, Gabarra, L, Ganga, K, Garcia-Bellido, J, Giacomini, F, Gozaliasl, G, Gwyn, S, Hildebrandt, H, Huertas-Company, M, Jimenez Munoz, A, Kajava, J, Kansal, V, Kirkpatrick, C, Legrand, L, Loureiro, A, MacIas-Perez, J, Magliocchetti, M, Mainetti, G, Maoli, R, Martinelli, M, Martins, C, Matthew, S, Maturi, M, Maurin, L, Metcalf, R, Migliaccio, M, Monaco, P, Morgante, G, Nadathur, S, Patrizii, L, Pezzotta, A, Porciani, C, Potter, D, Pontinen, M, Reimberg, P, Rocci, P, Sanchez, A, Schneider, A, Schultheis, M, Sefusatti, E, Simon, P, Spurio Mancini, A, Stanford, S, Steinwagner, J, Testera, G, Teyssier, R, Toft, S, Tosi, S, Troja, A, Tucci, M, Valiviita, J, Vergani, D, Lesci G. F., Sereno M., Radovich M., Castignani G., Bisigello L., Marulli F., Moscardini L., Baumont L., Covone G., Farrens S., Giocoli C., Ingoglia L., Miranda La Hera S., Vannier M., Biviano A., Maurogordato S., Aghanim N., Amara A., Andreon S., Auricchio N., Baldi M., Bardelli S., Bender R., Bodendorf C., Bonino D., Branchini E., Brescia M., Brinchmann J., Camera S., Capobianco V., Carbone C., Carretero J., Casas S., Castander F. J., Castellano M., Cavuoti S., Cimatti A., Congedo G., Conselice C. J., Conversi L., Copin Y., Corcione L., Courbin F., Courtois H. M., Da Silva A., Degaudenzi H., Di Giorgio A. M., Dinis J., Dubath F., Duncan C. A. J., Dupac X., Dusini S., Farina M., Ferriol S., Fosalba P., Fotopoulou S., Frailis M., Franceschi E., Franzetti P., Fumana M., Galeotta S., Garilli B., Gillis B., Grazian A., Grupp F., Haugan S. V. H., Hook I., Hormuth F., Hornstrup A., Hudelot P., Jahnke K., Kummel M., Kermiche S., Kiessling A., Kilbinger M., Kubik B., Kunz M., Kurki-Suonio H., Ligori S., Lilje P. B., Lindholm V., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Martinet N., Massey R., Medinaceli E., Melchior M., Mellier Y., Meneghetti M., Merlin E., Meylan G., Moresco M., Munari E., Nakajima R., Niemi S. -M., Padilla C., Paltani S., Pasian F., Pedersen K., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Pozzetti L., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Rossetti E., Saglia R., Sapone D., Sartoris B., Schirmer M., Schneider P., Secroun A., Seidel G., Serrano S., Sirignano C., Sirri G., Skottfelt J., Stanco L., Starck J. -L., Tallada-Crespi P., Taylor A. N., Teplitz H. I., Tereno I., Toledo-Moreo R., Torradeflot F., Tutusaus I., Valentijn E. A., Valenziano L., Vassallo T., Veropalumbo A., Wang Y., Weller J., Zacchei A., Zamorani G., Zoubian J., Zucca E., Bolzonella M., Bozzo E., Colodro-Conde C., Di Ferdinando D., Gracia-Carpio J., Marcin S., Mauri N., Neissner C., Nucita A. A., Sakr Z., Scottez V., Tenti M., Viel M., Wiesmann M., Akrami Y., Anselmi S., Baccigalupi C., Ballardini M., Borgani S., Borlaff A. S., Bruton S., Burigana C., Cabanac R., Calabro A., Cappi A., Carvalho C. S., Castro T., Canas-Herrera G., Chambers K. C., Cooray A. R., Coupon J., Cucciati O., Davini S., De La Torre S., De Lucia G., Desprez G., Di Domizio S., Dole H., Diaz-Sanchez A., Escartin Vigo J. A., Escoffier S., Ferrero I., Finelli F., Gabarra L., Ganga K., Garcia-Bellido J., Giacomini F., Gozaliasl G., Gwyn S., Hildebrandt H., Huertas-Company M., Jimenez Munoz A., Kajava J. J. E., Kansal V., Kirkpatrick C. C., Legrand L., Loureiro A., MacIas-Perez J., Magliocchetti M., Mainetti G., Maoli R., Martinelli M., Martins C. J. A. P., Matthew S., Maturi M., Maurin L., Metcalf R. B., Migliaccio M., Monaco P., Morgante G., Nadathur S., Patrizii L., Pezzotta A., Porciani C., Potter D., Pontinen M., Reimberg P., Rocci P. -F., Sanchez A. G., Schneider A., Schultheis M., Sefusatti E., Simon P., Spurio Mancini A., Stanford S. A., Steinwagner J., Testera G., Teyssier R., Toft S., Tosi S., Troja A., Tucci M., Valiviita J., and Vergani D.
Abstract: Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. Results. We calibrated galaxy selections using simulated ground-based griz and Euclid YEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high zl. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-bas
Published: 2024

42. Euclid preparation: XXXIX. The effect of baryons on the halo mass function

Author: Castro, T, Borgani, S, Costanzi, M, Dakin, J, Dolag, K, Fumagalli, A, Ragagnin, A, Saro, A, Le Brun, A, Aghanim, N, Amara, A, Andreon, S, Auricchio, N, Baldi, M, Bardelli, S, Bodendorf, C, Bonino, D, Branchini, E, Brescia, M, Brinchmann, J, Camera, S, Capobianco, V, Carbone, C, Carretero, J, Casas, S, Castellano, M, Cavuoti, S, Cimatti, A, Congedo, G, Conselice, C, Conversi, L, Copin, Y, Corcione, L, Courbin, F, Courtois, H, Cropper, M, Da Silva, A, Degaudenzi, H, Di Giorgio, A, Dinis, J, Dubath, F, Duncan, C, Dupac, X, Farina, M, Farrens, S, Ferriol, S, Frailis, M, Franceschi, E, Fumana, M, Galeotta, S, Gillis, B, Giocoli, C, Grazian, A, Grupp, F, Haugan, S, Holmes, W, Hormuth, F, Hornstrup, A, Jahnke, K, Keihanen, E, Kermiche, S, Kiessling, A, Kilbinger, M, Kubik, B, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, P, Lindholm, V, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Martinet, N, Marulli, F, Massey, R, Maurogordato, S, Medinaceli, E, Meneghetti, M, Merlin, E, Meylan, G, Moresco, M, Moscardini, L, Munari, E, Niemi, S, Padilla, C, Paltani, S, Pasian, F, Pettorino, V, Pires, S, Polenta, G, Poncet, M, Popa, L, Pozzetti, L, Raison, F, Rebolo, R, Renzi, A, Rhodes, J, Riccio, G, Romelli, E, Roncarelli, M, Saglia, R, Sapone, D, Sartoris, B, Schneider, P, Schrabback, T, Secroun, A, Seidel, G, Serrano, S, Sirignano, C, Sirri, G, Stanco, L, Starck, J, Tallada-Crespi, P, Taylor, A, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, E, Valenziano, L, Vassallo, T, Veropalumbo, A, Wang, Y, Weller, J, Zacchei, A, Zamorani, G, Zoubian, J, Zucca, E, Biviano, A, Bozzo, E, Cerna, C, Colodro-Conde, C, Di Ferdinando, D, Mauri, N, Neissner, C, Sakr, Z, Scottez, V, Tenti, M, Viel, M, Wiesmann, M, Akrami, Y, Anselmi, S, Baccigalupi, C, Ballardini, M, Borlaff, A, Bruton, S, Burigana, C, Cabanac, R, Cappi, A, Carvalho, C, Castignani, G, Canas-Herrera, G, Chambers, K, Cooray, A, Coupon, J, Cucciati, O, Diaz-Sanchez, A, Davini, S, De La Torre, S, De Lucia, G, Desprez, G, Di Domizio, S, Dole, H, Escoffier, S, Ferrero, I, Finelli, F, Gabarra, L, Ganga, K, Garcia-Bellido, J, Giacomini, F, Gozaliasl, G, Hildebrandt, H, Ilic, S, Jimanez Munnoz, A, Kajava, J, Kansal, V, Kirkpatrick, C, Legrand, L, Loureiro, A, MacIas-Perez, J, Magliocchetti, M, Mainetti, G, Maoli, R, Martinelli, M, Martins, C, Matthew, S, Maturi, M, Maurin, L, Metcalf, R, Migliaccio, M, Monaco, P, Morgante, G, Nadathur, S, Patrizii, L, Pezzotta, A, Popa, V, Porciani, C, Potter, D, Pontinen, M, Reimberg, P, Rocci, P, Sanchez, A, Schaye, J, Schneider, A, Sefusatti, E, Sereno, M, Simon, P, Spurio Mancini, A, Stadel, J, Stanford, S, Steinwagner, J, Testera, G, Tewes, M, Teyssier, R, Toft, S, Tosi, S, Troja, A, Tucci, M, Valiviita, J, Vergani, D, Castro T., Borgani S., Costanzi M., Dakin J., Dolag K., Fumagalli A., Ragagnin A., Saro A., Le Brun A. M. C., Aghanim N., Amara A., Andreon S., Auricchio N., Baldi M., Bardelli S., Bodendorf C., Bonino D., Branchini E., Brescia M., Brinchmann J., Camera S., Capobianco V., Carbone C., Carretero J., Casas S., Castellano M., Cavuoti S., Cimatti A., Congedo G., Conselice C. J., Conversi L., Copin Y., Corcione L., Courbin F., Courtois H. M., Cropper M., Da Silva A., Degaudenzi H., Di Giorgio A. M., Dinis J., Dubath F., Duncan C. A. J., Dupac X., Farina M., Farrens S., Ferriol S., Frailis M., Franceschi E., Fumana M., Galeotta S., Gillis B., Giocoli C., Grazian A., Grupp F., Haugan S. V. H., Holmes W., Hormuth F., Hornstrup A., Jahnke K., Keihanen E., Kermiche S., Kiessling A., Kilbinger M., Kubik B., Kunz M., Kurki-Suonio H., Ligori S., Lilje P. B., Lindholm V., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Martinet N., Marulli F., Massey R., Maurogordato S., Medinaceli E., Meneghetti M., Merlin E., Meylan G., Moresco M., Moscardini L., Munari E., Niemi S. -M., Padilla C., Paltani S., Pasian F., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Pozzetti L., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Saglia R., Sapone D., Sartoris B., Schneider P., Schrabback T., Secroun A., Seidel G., Serrano S., Sirignano C., Sirri G., Stanco L., Starck J. -L., Tallada-Crespi P., Taylor A. N., Tereno I., Toledo-Moreo R., Torradeflot F., Tutusaus I., Valentijn E. A., Valenziano L., Vassallo T., Veropalumbo A., Wang Y., Weller J., Zacchei A., Zamorani G., Zoubian J., Zucca E., Biviano A., Bozzo E., Cerna C., Colodro-Conde C., Di Ferdinando D., Mauri N., Neissner C., Sakr Z., Scottez V., Tenti M., Viel M., Wiesmann M., Akrami Y., Anselmi S., Baccigalupi C., Ballardini M., Borlaff A. S., Bruton S., Burigana C., Cabanac R., Cappi A., Carvalho C. S., Castignani G., Canas-Herrera G., Chambers K. C., Cooray A. R., Coupon J., Cucciati O., Diaz-Sanchez A., Davini S., De La Torre S., De Lucia G., Desprez G., Di Domizio S., Dole H., Escoffier S., Ferrero I., Finelli F., Gabarra L., Ganga K., Garcia-Bellido J., Giacomini F., Gozaliasl G., Hildebrandt H., Ilic S., Jimanez Munnoz A., Kajava J. J. E., Kansal V., Kirkpatrick C. C., Legrand L., Loureiro A., MacIas-Perez J., Magliocchetti M., Mainetti G., Maoli R., Martinelli M., Martins C. J. A. P., Matthew S., Maturi M., Maurin L., Metcalf R. B., Migliaccio M., Monaco P., Morgante G., Nadathur S., Patrizii L., Pezzotta A., Popa V., Porciani C., Potter D., Pontinen M., Reimberg P., Rocci P. -F., Sanchez A. G., Schaye J., Schneider A., Sefusatti E., Sereno M., Simon P., Spurio Mancini A., Stadel J., Stanford S. A., Steinwagner J., Testera G., Tewes M., Teyssier R., Toft S., Tosi S., Troja A., Tucci M., Valiviita J., Vergani D., Castro, T, Borgani, S, Costanzi, M, Dakin, J, Dolag, K, Fumagalli, A, Ragagnin, A, Saro, A, Le Brun, A, Aghanim, N, Amara, A, Andreon, S, Auricchio, N, Baldi, M, Bardelli, S, Bodendorf, C, Bonino, D, Branchini, E, Brescia, M, Brinchmann, J, Camera, S, Capobianco, V, Carbone, C, Carretero, J, Casas, S, Castellano, M, Cavuoti, S, Cimatti, A, Congedo, G, Conselice, C, Conversi, L, Copin, Y, Corcione, L, Courbin, F, Courtois, H, Cropper, M, Da Silva, A, Degaudenzi, H, Di Giorgio, A, Dinis, J, Dubath, F, Duncan, C, Dupac, X, Farina, M, Farrens, S, Ferriol, S, Frailis, M, Franceschi, E, Fumana, M, Galeotta, S, Gillis, B, Giocoli, C, Grazian, A, Grupp, F, Haugan, S, Holmes, W, Hormuth, F, Hornstrup, A, Jahnke, K, Keihanen, E, Kermiche, S, Kiessling, A, Kilbinger, M, Kubik, B, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, P, Lindholm, V, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Martinet, N, Marulli, F, Massey, R, Maurogordato, S, Medinaceli, E, Meneghetti, M, Merlin, E, Meylan, G, Moresco, M, Moscardini, L, Munari, E, Niemi, S, Padilla, C, Paltani, S, Pasian, F, Pettorino, V, Pires, S, Polenta, G, Poncet, M, Popa, L, Pozzetti, L, Raison, F, Rebolo, R, Renzi, A, Rhodes, J, Riccio, G, Romelli, E, Roncarelli, M, Saglia, R, Sapone, D, Sartoris, B, Schneider, P, Schrabback, T, Secroun, A, Seidel, G, Serrano, S, Sirignano, C, Sirri, G, Stanco, L, Starck, J, Tallada-Crespi, P, Taylor, A, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, E, Valenziano, L, Vassallo, T, Veropalumbo, A, Wang, Y, Weller, J, Zacchei, A, Zamorani, G, Zoubian, J, Zucca, E, Biviano, A, Bozzo, E, Cerna, C, Colodro-Conde, C, Di Ferdinando, D, Mauri, N, Neissner, C, Sakr, Z, Scottez, V, Tenti, M, Viel, M, Wiesmann, M, Akrami, Y, Anselmi, S, Baccigalupi, C, Ballardini, M, Borlaff, A, Bruton, S, Burigana, C, Cabanac, R, Cappi, A, Carvalho, C, Castignani, G, Canas-Herrera, G, Chambers, K, Cooray, A, Coupon, J, Cucciati, O, Diaz-Sanchez, A, Davini, S, De La Torre, S, De Lucia, G, Desprez, G, Di Domizio, S, Dole, H, Escoffier, S, Ferrero, I, Finelli, F, Gabarra, L, Ganga, K, Garcia-Bellido, J, Giacomini, F, Gozaliasl, G, Hildebrandt, H, Ilic, S, Jimanez Munnoz, A, Kajava, J, Kansal, V, Kirkpatrick, C, Legrand, L, Loureiro, A, MacIas-Perez, J, Magliocchetti, M, Mainetti, G, Maoli, R, Martinelli, M, Martins, C, Matthew, S, Maturi, M, Maurin, L, Metcalf, R, Migliaccio, M, Monaco, P, Morgante, G, Nadathur, S, Patrizii, L, Pezzotta, A, Popa, V, Porciani, C, Potter, D, Pontinen, M, Reimberg, P, Rocci, P, Sanchez, A, Schaye, J, Schneider, A, Sefusatti, E, Sereno, M, Simon, P, Spurio Mancini, A, Stadel, J, Stanford, S, Steinwagner, J, Testera, G, Tewes, M, Teyssier, R, Toft, S, Tosi, S, Troja, A, Tucci, M, Valiviita, J, Vergani, D, Castro T., Borgani S., Costanzi M., Dakin J., Dolag K., Fumagalli A., Ragagnin A., Saro A., Le Brun A. M. C., Aghanim N., Amara A., Andreon S., Auricchio N., Baldi M., Bardelli S., Bodendorf C., Bonino D., Branchini E., Brescia M., Brinchmann J., Camera S., Capobianco V., Carbone C., Carretero J., Casas S., Castellano M., Cavuoti S., Cimatti A., Congedo G., Conselice C. J., Conversi L., Copin Y., Corcione L., Courbin F., Courtois H. M., Cropper M., Da Silva A., Degaudenzi H., Di Giorgio A. M., Dinis J., Dubath F., Duncan C. A. J., Dupac X., Farina M., Farrens S., Ferriol S., Frailis M., Franceschi E., Fumana M., Galeotta S., Gillis B., Giocoli C., Grazian A., Grupp F., Haugan S. V. H., Holmes W., Hormuth F., Hornstrup A., Jahnke K., Keihanen E., Kermiche S., Kiessling A., Kilbinger M., Kubik B., Kunz M., Kurki-Suonio H., Ligori S., Lilje P. B., Lindholm V., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Martinet N., Marulli F., Massey R., Maurogordato S., Medinaceli E., Meneghetti M., Merlin E., Meylan G., Moresco M., Moscardini L., Munari E., Niemi S. -M., Padilla C., Paltani S., Pasian F., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Pozzetti L., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Saglia R., Sapone D., Sartoris B., Schneider P., Schrabback T., Secroun A., Seidel G., Serrano S., Sirignano C., Sirri G., Stanco L., Starck J. -L., Tallada-Crespi P., Taylor A. N., Tereno I., Toledo-Moreo R., Torradeflot F., Tutusaus I., Valentijn E. A., Valenziano L., Vassallo T., Veropalumbo A., Wang Y., Weller J., Zacchei A., Zamorani G., Zoubian J., Zucca E., Biviano A., Bozzo E., Cerna C., Colodro-Conde C., Di Ferdinando D., Mauri N., Neissner C., Sakr Z., Scottez V., Tenti M., Viel M., Wiesmann M., Akrami Y., Anselmi S., Baccigalupi C., Ballardini M., Borlaff A. S., Bruton S., Burigana C., Cabanac R., Cappi A., Carvalho C. S., Castignani G., Canas-Herrera G., Chambers K. C., Cooray A. R., Coupon J., Cucciati O., Diaz-Sanchez A., Davini S., De La Torre S., De Lucia G., Desprez G., Di Domizio S., Dole H., Escoffier S., Ferrero I., Finelli F., Gabarra L., Ganga K., Garcia-Bellido J., Giacomini F., Gozaliasl G., Hildebrandt H., Ilic S., Jimanez Munnoz A., Kajava J. J. E., Kansal V., Kirkpatrick C. C., Legrand L., Loureiro A., MacIas-Perez J., Magliocchetti M., Mainetti G., Maoli R., Martinelli M., Martins C. J. A. P., Matthew S., Maturi M., Maurin L., Metcalf R. B., Migliaccio M., Monaco P., Morgante G., Nadathur S., Patrizii L., Pezzotta A., Popa V., Porciani C., Potter D., Pontinen M., Reimberg P., Rocci P. -F., Sanchez A. G., Schaye J., Schneider A., Sefusatti E., Sereno M., Simon P., Spurio Mancini A., Stadel J., Stanford S. A., Steinwagner J., Testera G., Tewes M., Teyssier R., Toft S., Tosi S., Troja A., Tucci M., Valiviita J., and Vergani D.
Abstract: The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are calibrated using non-radiative cosmological hydrodynamical simulations, and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable 1% relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model arising from baryonic corrections to cluster masses are subdominant when compared to the precision with which mass-observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters.
Published: 2024

43. EasyCritics II. Testing its efficiency: new gravitational lens candidates in CFHTLenS

Author: Carrasco, M., Stapelberg, S., Maturi, M., Bartelmann, M., Seidel, G., and Erben, T.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We report the results of $EasyCritics$, a fully automated algorithm for the efficient search of strong-lensing (SL) regions in wide-field surveys, applied to the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). By using only the photometric information of the brightest elliptical galaxies distributed over a wide redshift range ($\smash{0.2 \lesssim z \lesssim 0.9}$) and without requiring the identification of arcs, our algorithm produces lensing potential models and catalogs of critical curves of the entire survey area. We explore several parameter set configurations in order to test the efficiency of our approach. In a specific configuration, $EasyCritics$ generates only $\sim1200$ possibly super-critical regions in the CFHTLS area, drastically reducing the effective area for inspection from $154$ sq. deg to $\sim0.623$ sq. deg, $i.e.$ by more than two orders of magnitude. Among the pre-selected SL regions, we identify 32 of the 44 previously known lenses on the group and cluster scale, and discover 9 new promising lens candidates. The detection rate can be easily improved to $\sim82\%$ by a simple modification in the parameter set, but at the expense of increasing the total number of possible SL candidates. Note that $EasyCritics$ is fully complementary to other arc-finders since we characterize lenses instead of directly identifying arcs. Although future comparisons against numerical simulations are required for fully assessing the efficiency of $EasyCritics$, the algorithm seems very promising for upcoming surveys covering $\smash{10^{4}}$ sq. deg, such as the $Euclid$ mission and $LSST$, where the pre-selection of candidates for any kind of SL analysis will be indispensable due to the expected enormous data volume.
Published: 2018

44. Image simulations for gravitational lensing with SkyLens

Author: Plazas, A. A., Meneghetti, M., Maturi, M., and Rhodes, J.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present the latest version of the ray-tracing simulation code Skylens, which can be used to develop image simulations that reproduce strong lensing observations by any mass distribution with a high level of realism. Improvements of the code with respect to previous versions include the implementation of the multi-lens plane formalism, the use of denoised source galaxies from the Hubble eXtreme Deep Field, and the simulation of substructures in lensed arcs and images, based on a morphological analysis of bright nearby galaxies. Skylens can simulate observations with virtually any telescope. We present examples of space- and ground-based observations of a galaxy cluster through the Wide Field Channel on the Advanced Camera for Surveys of the Hubble Space Telescope, the Near Infrared Camera of the James Webb Space Telescope, the Wide Field Imager of the Wide Field Infrared Survey Telescope, the Hyper Suprime Camera of the Subaru telescope, and the Visible Imaging Channel of the Euclid space mission., Comment: Accepted for publication in the "Monthly Notices of the Royal Astronomical Society"
Published: 2018
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45. Searching for galaxy clusters in the Kilo-Degree Survey

Author: Radovich, M., Puddu, E., Bellagamba, F., Roncarelli, M., Moscardini, L., Bardelli, S., Grado, A., Getman, F., Maturi, M., Huang, Z., Napolitano, N., McFarland, J., Valentijn, E., and Bilicki, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ~ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. We obtained 1858 candidate clusters with redshift 0 < z_c < 0.7 and mass 13.5 < log(M500/Msun) < 15 in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available., Comment: 13 pages, 15 figures. Accepted for publication on Astronomy & Astrophysics
Published: 2017
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46. The miniJPAS survey. Optical detection of galaxy clusters with PZWav

Author: Doubrawa, L., primary, Cypriano, E.S., additional, Finoguenov, A., additional, Lopes, P.A., additional, Gonzalez, A.H., additional, Maturi, M., additional, Dupke, R.A., additional, Gonzalez Delgado, R.M., additional, Abramo, R., additional, Benitez, N., additional, Bonoli, S., additional, Carneiro, S., additional, Cenarro, J., additional, Cristóbal-Hornillos, D., additional, Ederoclite, A., additional, Hernan-Caballero, A., additional, López-Sanjuan, C., additional, Marín-Franch, A., additional, Mendes de Oliveira, C., additional, Moles, M., additional, Sodré, L., additional, Taylor, K., additional, Varela, J., additional, and Vazquez Ramio, H., additional
Published: 2024
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47. Imaging-guided photothermal therapy for bladder cancer lesions smaller than 1 millimeter expressing marker of bladder CIS

Author: Armanetti, P., primary, Locatelli, I., additional, Venegoni, C., additional, Alchera, E., additional, Pederzoli, F., additional, Maturi, M., additional, Locatelli, E., additional, Tortorella, S., additional, Curnis, F., additional, Corti, A., additional, Lucianò, R., additional, Sanvito, F., additional, Salonia, A., additional, Montorsi, F., additional, Moschini, M., additional, Popov, V., additional, Jose, J., additional, Comes Franchini, M., additional, Ooi, E.H., additional, and Alfano, M., additional
Published: 2024
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48. Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning

Author: Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Zinchenko, I. A., Euclid Collaboration, Aussel, B., Kruk, S., Walmsley, M., Huertas-Company, M., Castellano, M., Conselice, C. J., Veneri, M. Delli, Sánchez, H. Domínguez, Duc, P. -A., Kuchner, U., La Marca, A., Margalef-Bentabol, B., Marleau, F. R., Stevens, G., Toba, Y., Tortora, C., Wang, L., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mainetti, G., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bretonnière, H., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Cañas-Herrera, G., Chambers, K. C., Coupon, J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., George, K., Giacomini, F., Gozaliasl, G., Gregorio, A., Guinet, D., Hall, A., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Walton, Nicholas A., Peel, A., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Zinchenko, I. A.
Abstract: The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes such as disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowdsourcing. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images., Comment: 27 pages, 26 figures, 5 tables, submitted to A&A
Published: 2024

49. Euclid preparation. XXXVII.:Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Author: Collaboration, Euclid, Lesci, G. F., Sereno, M., Radovich, M., Castignani, G., Bisigello, L., Marulli, F., Moscardini, L., Baumont, L., Covone, G., Farrens, S., Giocoli, C., Ingoglia, L., Miranda La Hera, S., Vannier, M., Biviano, A., Maurogordato, S., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Massey, R., Medinaceli, E., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Munari, E., Nakajima, R., Niemi, S.-M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J.-L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Bolzonella, M., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bruton, S., Burigana, C., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Cucciati, O., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Escartin Vigo, J. A., Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Gwyn, S., Hildebrandt, H., Huertas-Company, M., Jimenez Muñoz, A., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P.-F., Sánchez, A. G., Schneider, A., Schultheis, M., Sefusatti, E., Simon, P., Spurio Mancini, A., Stanford, S. A., Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., Vergani, D., Collaboration, Euclid, Lesci, G. F., Sereno, M., Radovich, M., Castignani, G., Bisigello, L., Marulli, F., Moscardini, L., Baumont, L., Covone, G., Farrens, S., Giocoli, C., Ingoglia, L., Miranda La Hera, S., Vannier, M., Biviano, A., Maurogordato, S., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Massey, R., Medinaceli, E., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Munari, E., Nakajima, R., Niemi, S.-M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J.-L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Bolzonella, M., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Marcin, S., Mauri, N., Neissner, C., Nucita, A. A., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Borgani, S., Borlaff, A. S., Bruton, S., Burigana, C., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Cucciati, O., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Díaz-Sánchez, A., Escartin Vigo, J. A., Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Gwyn, S., Hildebrandt, H., Huertas-Company, M., Jimenez Muñoz, A., Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Porciani, C., Potter, D., Pöntinen, M., Reimberg, P., Rocci, P.-F., Sánchez, A. G., Schneider, A., Schultheis, M., Sefusatti, E., Simon, P., Spurio Mancini, A., Stanford, S. A., Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., and Vergani, D.
Abstract: Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. Results. We calibrated galaxy selections using simulated ground-based griz and EuclidYEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections ap
Published: 2024

50. AMICO-COSMOS galaxy cluster and group catalogue up to z = 2: Sample properties and X-ray counterparts.

Author: Toni, G., Maturi, M., Finoguenov, A., Moscardini, L., and Castignani, G.
Subjects: *GALACTIC evolution, *MATCHED filters, *GALACTIC magnitudes, *X-rays, *GALAXY clusters, UNIVERSE
Abstract: Aims. We present the results of a new galaxy cluster search in the COSMOS field obtained using the Adaptive Matched Identifier of Clustered Objects (AMICO). Our aim was to produce a new cluster and group catalogue up to z = 2 by performing an innovative application of AMICO with respect to previous successful applications to wide-field surveys in terms of depth (down to r < 26.7), small area covered (∼1.69 deg2 of unmasked effective area), and redshift extent. This sample and the comparative analysis we performed with the X-rays, allowed for the calibration of mass-proxy scaling relations up to z = 2 and down to less than 1013 M⊙, and constitutes the basis for the refinement of the cluster model for future applications of AMICO, such as the analysis of upcoming Euclid data. Methods. The AMICO algorithm is based on an optimal linear matched filter and detects clusters in photometric galaxy catalogues using galaxy location, photometric redshift and, in the simplest case, one galaxy property. We chose to use a single magnitude as the galaxy property, avoiding the explicit use of galaxy colour for the selection of clusters. We used three different magnitudes by performing three independent runs in the r-, Y-, and H-bands using both COSMOS2020 and COSMOS2015 galaxy catalogues. We created a composite visibility mask and cluster models for the signal to detect, and we estimated the noise directly from the data. Results. We performed a matching of the catalogues resulting from the three runs and merged them to produce a final catalogue that contains 1269 and 666 candidate clusters and groups with S/N > 3.0 and > 3.5, respectively. A total of 490 candidates are detected in all three runs. Most of the detections unmatched between runs have S/N < 3.5, which can be set as a threshold for selecting a more robust sample. We assigned X-ray properties to our detections by matching the catalogue with a public X-ray selected group sample and by estimating, for unmatched detections, the X-ray properties at the location of AMICO candidates using Chandra+XMM-Newton data. There are in total 622 candidate clusters and groups with an X-ray flux estimate. This large sample of candidates with X-ray properties allowed the calibration of the scaling relations between two AMICO mass-proxies (richness and cluster amplitude) and X-ray mass and the study of their redshift dependence for the selection of the most stable photometric bands. [ABSTRACT FROM AUTHOR]
Published: 2024
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