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326 results on '"Tavagnacco, D."'

1. Euclid preparation. Simulations and nonlinearities beyond $\Lambda$CDM. 1. Numerical methods and validation

Author: Euclid Collaboration, Adamek, J., Fiorini, B., Baldi, M., Brando, G., Breton, M. -A., Hassani, F., Koyama, K., Brun, A. M. C. Le, Rácz, G., Winther, H. -A., Casalino, A., Hernández-Aguayo, C., Li, B., Potter, D., Altamura, E., Carbone, C., Giocoli, C., Mota, D. F., Pourtsidou, A., Sakr, Z., Vernizzi, F., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Bardelli, S., Battaglia, P., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., 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., Da Silva, A., Degaudenzi, H., De Lucia, G., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., 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., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Neissner, C., 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., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Finelli, F., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Balaguera-Antolinez, A., Ballardini, M., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, 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, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Kruk, S., Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Migliaccio, M., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Patrizii, L., Popa, V., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Silvestri, A., Mancini, A. Spurio, Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Vielzeuf, P., and Walton, N. A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: To constrain models beyond $\Lambda$CDM, the development of the Euclid analysis pipeline requires simulations that capture the nonlinear phenomenology of such models. We present an overview of numerical methods and $N$-body simulation codes developed to study the nonlinear regime of structure formation in alternative dark energy and modified gravity theories. We review a variety of numerical techniques and approximations employed in cosmological $N$-body simulations to model the complex phenomenology of scenarios beyond $\Lambda$CDM. This includes discussions on solving nonlinear field equations, accounting for fifth forces, and implementing screening mechanisms. Furthermore, we conduct a code comparison exercise to assess the reliability and convergence of different simulation codes across a range of models. Our analysis demonstrates a high degree of agreement among the outputs of different simulation codes, providing confidence in current numerical methods for modelling cosmic structure formation beyond $\Lambda$CDM. We highlight recent advances made in simulating the nonlinear scales of structure formation, which are essential for leveraging the full scientific potential of the forthcoming observational data from the Euclid mission., Comment: 20 pages, 7 figures, 1 appendix; submitted on behalf of the Euclid Collaboration
Published: 2024

2. 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

3. Euclid: The Early Release Observations Lens Search Experiment

Author: Barroso, J. A. Acevedo, O'Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Busillo, V., Andika, I. T., Cabanac, R., Courtois, H. M., Crook-Mansour, J., Delchambre, L., Despali, G., Ecker, L. R., Franco, A., Holloway, P., Jackson, N., Jahnke, K., Mahler, G., Marchetti, L., Matavulj, P., Melo, A., Meneghetti, M., Moustakas, L. A., Müller, O., Nucita, A. A., Paulino-Afonso, A., Pearson, J., Rojas, K., Scarlata, C., Schuldt, S., Serjeant, S., Sluse, D., Suyu, S. H., Vaccari, M., Verma, A., Vernardos, G., Walmsley, M., Bouy, H., Walth, G. L., Powell, D. M., Bolzonella, M., Cuillandre, J. -C., Kluge, M., Saifollahi, T., Schirmer, M., Stone, C., Acebron, A., Bazzanini, L., Díaz-Sánchez, A., Hogg, N. B., Koopmans, L. V. E., Kruk, S., Leuzzi, L., Manjón-García, A., Mannucci, F., Nagam, B. C., Pearce-Casey, R., Scharré, L., Wilde, J., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Battaglia, P., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., George, K., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Nichol, R. C., 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., Sakr, Z., Sánchez, A. G., Sapone, D., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zucca, E., Burigana, C., Scottez, V., and Viel, M.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A
Published: 2024

4. 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

5. Euclid preparation. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods

Author: Euclid Collaboration, Enia, A., Bolzonella, M., Pozzetti, L., Humphrey, A., Cunha, P. A. C., Hartley, W. G., Dubath, F., Paltani, S., Lopez, X. Lopez, Quai, S., Bardelli, S., Bisigello, L., Cavuoti, S., De Lucia, G., Ginolfi, M., Grazian, A., Siudek, M., Tortora, C., Zamorani, G., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., 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., Castignani, G., Cimatti, A., Colodro-Conde, C., 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., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Pasian, F., Pedersen, K., 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., Sakr, Z., Sapone, D., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zucca, E., Biviano, A., Boucaud, A., Burigana, C., Calabrese, M., Vigo, J. A. Escartin, Gracia-Carpio, J., Mauri, N., Pezzotta, A., Pöntinen, M., Porciani, C., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bergamini, P., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Cucciati, O., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Hall, A., Hemmati, S., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., 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., Simon, P., Mancini, A. Spurio, 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., Zinchenko, I. A., Rodighiero, G., and Talia, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous Machine Learning algorithms have been presented for computing their photometric redshifts and physical parameters (PP), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information, to the point where the recovery of some well-established physical relationships between parameters might not be guaranteed. To forecast the reliability of Euclid photo-$z$s and PPs calculations, we produced two mock catalogs simulating Euclid photometry. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF). We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-$z$s, stellar masses, star-formation rates, and the SFMS. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels. For the EWS, we found that the best results are achieved with a Mixed Labels approach, training the models with Wide survey features and labels from the Phosphoros results on deeper photometry, i.e., with the best possible set of labels for a given photometry. This imposes a prior, helping the models to better discern cases in degenerate regions of feature space, i.e., when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than $3\%$ performance degradation using a COSMOS-like reference sample or removing $u$ band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-$z$, PPs, and the SFMS., Comment: 26 pages, 13 figures. Submitted to A&A
Published: 2024

6. Euclid preparation. Observational expectations for redshift z<7 active galactic nuclei in the Euclid Wide and Deep surveys

Author: Euclid Collaboration, Selwood, M., Fotopoulou, S., Bremer, M. N., Bisigello, L., Landt, H., Bañados, E., Zamorani, G., Shankar, F., Stern, D., Lusso, E., Spinoglio, L., Allevato, V., Ricci, F., Feltre, A., Mannucci, F., Salvato, M., Bowler, R. A. A., Mignoli, M., Vergani, D., La Franca, F., 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., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., 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., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., 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., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., 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., Rhodes, J., Riccio, G., Rix, Hans-Walter, Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zucca, E., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Colodro-Conde, C., De Lucia, G., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., George, K., Gracia-Carpio, J., Martinelli, M., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Cucciati, O., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferrero, I., Finelli, F., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hall, A., Hildebrandt, H., Hjorth, J., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Nicastro, L., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Verza, G., Weaver, J. R., and Zinchenko, I. A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We forecast the expected population of active galactic nuclei (AGN) observable in the Euclid Wide Survey (EWS) and Euclid Deep Survey (EDS). Starting from an X-ray luminosity function (XLF) we generate volume-limited samples of the AGN expected in the survey footprints. Each AGN is assigned an SED appropriate for its X-ray luminosity and redshift, with perturbations sampled from empirical distributions. The photometric detectability of each AGN is assessed via mock observation of the assigned SED. We estimate 40 million AGN will be detectable in at least one band in the EWS and 0.24 million in the EDS, corresponding to surface densities of 2.8$\times$10$^{3}$ deg$^{-2}$ and 4.7$\times$10$^{3}$ deg$^{-2}$. Employing colour selection criteria on our simulated data we select a sample of 4.8$\times$10$^{6}$ (331 deg$^{-2}$) AGN in the EWS and 1.7$\times$10$^{4}$ (346 deg$^{-2}$) in the EDS, amounting to 10% and 8% of the AGN detectable in the EWS and EDS. Including ancillary Rubin/LSST bands improves the completeness and purity of AGN selection. These data roughly double the total number of selected AGN to comprise 21% and 15% of the detectable AGN in the EWS and EDS. The total expected sample of colour-selected AGN contains 6.0$\times$10$^{6}$ (74%) unobscured AGN and 2.1$\times$10$^{6}$ (26%) obscured AGN, covering $0.02 \leq z \lesssim 5.2$ and $43 \leq \log_{10} (L_{bol} / erg s^{-1}) \leq 47$. With this simple colour selection, expected surface densities are already comparable to the yield of modern X-ray and mid-infrared surveys of similar area. The relative uncertainty on our expectation for detectable AGN is 6.7% for the EWS and 12.5% for the EDS, driven by the uncertainty of the XLF., Comment: 36 pages, 21 figures, submitted to A&A
Published: 2024

7. Euclid: Early Release Observations -- Deep anatomy of nearby galaxies

Author: Hunt, L. K., Annibali, F., Cuillandre, J. -C., Ferguson, A. M. N., Jablonka, P., Larsen, S. S., Marleau, F. R., Schinnerer, E., Schirmer, M., Stone, C., Tortora, C., Saifollahi, T., Lançon, A., Bolzonella, M., Gwyn, S., Kluge, M., Laureijs, R., Carollo, D., Collins, M. L. M., Dimauro, P., Duc, P. -A., Erkal, D., Howell, J. M., Nally, C., Saremi, E., Scaramella, R., Belokurov, V., Conselice, C. J., Knapen, J. H., McConnachie, A. W., McDonald, I., Carretero, J. Miro, Roman, J., Sauvage, M., Sola, E., Aghanim, N., Altieri, B., Andreon, S., Auricchio, N., Awan, S., Azzollini, R., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., 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., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., 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., Racca, G. D., Raison, F., Rebolo, R., Refregier, A., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Williams, O. R., Zamorani, G., Zucca, E., Burigana, C., De Lucia, G., George, K., Scottez, V., Miluzio, M., Simon, P., Mora, A., Martín-Fleitas, J., and Scott, D.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Euclid is poised to make significant advances in the study of nearby galaxies in the local Universe. Here we present a first look at 6 galaxies observed for the Nearby Galaxy Showcase as part of the Euclid Early Release Observations acquired between August and November, 2023. These targets, 3 dwarf galaxies (HolmbergII, IC10, NGC6822) and 3 spirals (IC342, NGC2403, NGC6744), range in distance from about 0.5 Mpc to 8.8 Mpc. Our assessment of the surface brightness depths in the stacked Euclid images confirms previous estimates in 100 arcsec^2 regions of 1sigma=30.5 mag/arcsec^2 for VIS, but slightly deeper than previous estimates for NISP with 1sigma=29.2-29.4 mag/arcsec^2. By combining Euclid HE, YE, and IE into RGB images, we illustrate the large field-of-view covered by a single Reference Observing Sequence, together with exquisite detail on parsec scales in these nearby galaxies. Radial surface brightness and color profiles demonstrate galaxy colors in agreement with stellar population synthesis models. Standard stellar photometry selection techniques find approximately 1.3 million stars across the 6 galaxy fields. Euclid's resolved stellar photometry allows us to constrain the star-formation histories of these galaxies, by disentangling the distributions of young stars, as well as asymptotic giant branch and red giant branch stellar populations. We finally examine 2 galaxies individually for surrounding satellite systems. Our analysis of the ensemble of dwarf satellites around NGC6744 reveals a new galaxy, EDwC1, a nucleated dwarf spheroidal at the end of a spiral arm. Our new census of the globular clusters around NGC2403 yields 9 new star-cluster candidates, 8 of which with colors indicative of evolved stellar populations. In summary, our investigation of the 6 Showcase galaxies demonstrates that Euclid is a powerful probe of the anatomy of nearby galaxies [abridged]., Comment: 36 pages; 20 figures in main text; 4 Appendices. Submitted to A&A, as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations
Published: 2024

8. Euclid. IV. The NISP Calibration Unit

Author: Euclid Collaboration, Hormuth, F., Jahnke, K., Schirmer, M., Lee, C. G. -Y., Scott, T., Barbier, R., Ferriol, S., Gillard, W., Grupp, F., Holmes, R., Holmes, W., Kubik, B., Macias-Perez, J., Laurent, M., Marpaud, J., Marton, M., Medinaceli, E., Morgante, G., Toledo-Moreo, R., Trifoglio, M., Rix, Hans-Walter, Secroun, A., Seiffert, M., Stassi, P., Wachter, S., Gutierrez, C. M., Vescovi, C., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Battaglia, P., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, R., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Douspis, M., Dubath, F., Ducret, F., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Faustini, F., Fotopoulou, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., George, K., Gillis, B., Giocoli, C., Grazian, A., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Hook, I., Hornstrup, A., Hudelot, P., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kümmel, M., Kunz, M., Kurki-Suonio, H., Mignant, D. Le, 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., Maurogordato, S., McCracken, H. J., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Morris, P. W., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paech, K., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., 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., Rusholme, B., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Sauvage, M., Schewtschenko, J. A., Schneider, P., Schrabback, T., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Smadja, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zerbi, F. M., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Kazandjian, M. V., Mauri, N., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Aubourg, E., Ballardini, M., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Borlaff, A. S., Borsato, E., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Casenove, P., Castro, T., Chambers, K. C., Charles, Y., Contarini, S., Cooray, A. R., Cucciati, O., Davini, S., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Huertas-Company, M., Ilbert, O., Jacobson, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Laudisio, F., Legrand, L., Libet, G., Loureiro, A., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Miluzio, M., Moretti, C., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Risso, I., Rocci, P. -F., Rollins, R. P., Sahlén, M., Scarlata, C., Schneider, A., Schultheis, M., Sereno, M., Shulevski, A., Silvestri, A., Simon, P., Mancini, A. Spurio, Stadel, J., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Zalesky, L., Archidiacono, M., Atrio-Barandela, F., Bouvard, T., Caro, F., Dimauro, P., Fang, Y., Ferguson, A. M. N., Finoguenov, A., Gasparetto, T., Brun, A. M. C. Le, Graet, J. Le, Liaudat, T. I., Montoro, A., Murray, C., Oguri, M., Pagano, L., Paoletti, D., Sarpa, E., Tanidis, K., Vernizzi, F., Viitanen, A., Kova{č}ić, I., Lesgourgues, J., Martín-Fleitas, J., and Mora, A.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The near-infrared calibration unit (NI-CU) on board Euclid's Near-Infrared Spectrometer and Photometer (NISP) is the first astronomical calibration lamp based on light-emitting diodes (LEDs) to be operated in space. Euclid is a mission in ESA's Cosmic Vision 2015-2025 framework, to explore the dark universe and provide a next-level characterisation of the nature of gravitation, dark matter, and dark energy. Calibrating photometric and spectrometric measurements of galaxies to better than 1.5% accuracy in a survey homogeneously mapping ~14000 deg^2 of extragalactic sky requires a very detailed characterisation of near-infrared (NIR) detector properties, as well their constant monitoring in flight. To cover two of the main contributions - relative pixel-to-pixel sensitivity and non-linearity characteristics - as well as support other calibration activities, NI-CU was designed to provide spatially approximately homogeneous (<12% variations) and temporally stable illumination (0.1%-0.2% over 1200s) over the NISP detector plane, with minimal power consumption and energy dissipation. NI-CU is covers the spectral range ~[900,1900] nm - at cryo-operating temperature - at 5 fixed independent wavelengths to capture wavelength-dependent behaviour of the detectors, with fluence over a dynamic range of >=100 from ~15 ph s^-1 pixel^-1 to >1500 ph s^-1 pixel^-1. For this functionality, NI-CU is based on LEDs. We describe the rationale behind the decision and design process, describe the challenges in sourcing the right LEDs, as well as the qualification process and lessons learned. We also provide a description of the completed NI-CU, its capabilities and performance as well as its limits. NI-CU has been integrated into NISP and the Euclid satellite, and since Euclid's launch in July 2023 has started supporting survey operations., Comment: Paper accepted for publication in A&A as part of the special issue 'Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations
Published: 2024

9. Euclid. III. The NISP Instrument

Author: Euclid Collaboration, Jahnke, K., Gillard, W., Schirmer, M., Ealet, A., Maciaszek, T., Prieto, E., Barbier, R., Bonoli, C., Corcione, L., Dusini, S., Grupp, F., Hormuth, F., Ligori, S., Martin, L., Morgante, G., Padilla, C., Toledo-Moreo, R., Trifoglio, M., Valenziano, L., Bender, R., Castander, F. J., Garilli, B., Lilje, P. B., Rix, H. -W., Auricchio, N., Balestra, A., Barriere, J. -C., Battaglia, P., Berthe, M., Bodendorf, C., Boenke, T., Bon, W., Bonnefoi, A., Caillat, A., Capobianco, V., Carle, M., Casas, R., Cho, H., Costille, A., Ducret, F., Ferriol, S., Franceschi, E., Gimenez, J. -L., Holmes, W., Hornstrup, A., Jhabvala, M., Kohley, R., Kubik, B., Laureijs, R., Mignant, D. Le, Lloro, I., Medinaceli, E., Mellier, Y., Polenta, G., Racca, G. D., Renzi, A., Salvignol, J. -C., Secroun, A., Seidel, G., Seiffert, M., Sirignano, C., Sirri, G., Strada, P., Smadja, G., Stanco, L., Wachter, S., Anselmi, S., Borsato, E., Caillat, L., Cogato, F., Colodro-Conde, C., Crouzet, P. -E., Conforti, V., D'Alessandro, M., Copin, Y., Cuillandre, J. -C., Davies, J. E., Davini, S., Derosa, A., Diaz, J. J., Di Domizio, S., Di Ferdinando, D., Farinelli, R., Ferrari, A. G., Fornari, F., Gabarra, L., Gutierrez, C. M., Giacomini, F., Lagier, P., Gianotti, F., Krause, O., Madrid, F., Laudisio, F., Macias-Perez, J., Naletto, G., Niclas, M., Marpaud, J., Mauri, N., da Silva, R., Passalacqua, F., Paterson, K., Patrizii, L., Risso, I., Solheim, B. G. B., Scodeggio, M., Stassi, P., Steinwagner, J., Tenti, M., Testera, G., Travaglini, R., Tosi, S., Troja, A., Tubio, O., Valieri, C., Vescovi, C., Ventura, S., Aghanim, N., Altieri, B., Amara, A., Amiaux, J., Andreon, S., Aussel, H., Baldi, M., Bardelli, S., Basset, A., Bonchi, A., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Chabaud, P. -Y., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Courbin, F., Courtois, H. M., Cropper, M., Cuby, J. -G., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Fabricius, M., Farina, M., Farrens, S., Faustini, F., Fosalba, P., Fotopoulou, S., Fourmanoit, N., Frailis, M., Franzetti, P., Galeotta, S., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Guzzo, L., Hailey, M., Haugan, S. V. H., Hoar, J., Hoekstra, H., Hook, I., Hudelot, P., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Lindholm, V., Alvarez, J. Lorenzo, Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martignac, J., Martinet, N., Marulli, F., Massey, R., Masters, D. C., Maurogordato, S., McCracken, H. J., Mei, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Moscardini, L., Nakajima, R., Nichol, R. C., Niemi, S. -M., Nutma, T., Paech, K., Paltani, S., Pasian, F., Peacock, J. A., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Refregier, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Rottgering, H. J. A., Saglia, R., Sapone, D., Sauvage, M., Scaramella, R., Schneider, P., Schrabback, T., Serrano, S., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Torradeflot, F., Tutusaus, I., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Vibert, D., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zerbi, F. M., Zoubian, J., Zucca, E., Appleton, P. N., Baccigalupi, C., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Casenove, P., Crocce, M., De Lucia, G., Vigo, J. A. Escartin, Fabbian, G., Finelli, F., George, K., Gracia-Carpio, J., Ilić, S., Liebing, P., Liu, C., Mainetti, G., Marcin, S., Martinelli, M., Morris, P. W., Neissner, C., Pezzotta, A., Pöntinen, M., Porciani, C., Sakr, Z., Scottez, V., Sefusatti, E., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Aubourg, E., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Calabro, A., Calderone, G., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Charles, Y., Chary, R., Colbert, J., Contarini, S., Contini, T., Cooray, A. R., Costanzi, M., Cucciati, O., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Dole, H., Escoffier, S., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Gozaliasl, G., Gregorio, A., Hall, A., Hartley, W. G., Hemmati, S., Hildebrandt, H., Hjorth, J., Hosseini, S., Huertas-Company, M., Ilbert, O., Jacobson, J., Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Lacasa, F., Brun, V. Le, Graet, J. Le, Legrand, L., Libet, G., Liu, S. J., Loureiro, A., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., McPartland, C. J. R., Metcalf, R. B., Migliaccio, M., Miluzio, M., Monaco, P., Moretti, C., Nadathur, S., Nicastro, L., Walton, Nicholas A., Odier, J., Oguri, M., Popa, V., Potter, D., Pourtsidou, A., Rocci, P. -F., Rollins, R. P., Rusholme, B., Sahlén, M., Sánchez, A. G., Scarlata, C., Schaye, J., Schewtschenko, J. A., Schneider, A., Schultheis, M., Sereno, M., Shankar, F., Shulevski, A., Sikkema, G., Silvestri, A., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Tanidis, K., Tao, C., Tessore, N., Teyssier, R., Toft, S., Tucci, M., Valiviita, J., Vergani, D., Vernizzi, F., Verza, G., Vielzeuf, P., Weaver, J. R., Zalesky, L., Zinchenko, I. A., Archidiacono, M., Atrio-Barandela, F., Bennett, C. L., Bouvard, T., Caro, F., Conseil, S., Dimauro, P., Duc, P. -A., Fang, Y., Ferguson, A. M. N., Gasparetto, T., Kova{č}ić, I., Kruk, S., Brun, A. M. C. Le, Liaudat, T. I., Montoro, A., Mora, A., Murray, C., Pagano, L., Paoletti, D., Radovich, M., Sarpa, E., Tommasi, E., Viitanen, A., Lesgourgues, J., Levi, M. E., and Martín-Fleitas, J.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The Near-Infrared Spectrometer and Photometer (NISP) on board the Euclid satellite provides multiband photometry and R>=450 slitless grism spectroscopy in the 950-2020nm wavelength range. In this reference article we illuminate the background of NISP's functional and calibration requirements, describe the instrument's integral components, and provide all its key properties. We also sketch the processes needed to understand how NISP operates and is calibrated, and its technical potentials and limitations. Links to articles providing more details and technical background are included. NISP's 16 HAWAII-2RG (H2RG) detectors with a plate scale of 0.3" pix^-1 deliver a field-of-view of 0.57deg^2. In photo mode, NISP reaches a limiting magnitude of ~24.5AB mag in three photometric exposures of about 100s exposure time, for point sources and with a signal-to-noise ratio (SNR) of 5. For spectroscopy, NISP's point-source sensitivity is a SNR = 3.5 detection of an emission line with flux ~2x10^-16erg/s/cm^2 integrated over two resolution elements of 13.4A, in 3x560s grism exposures at 1.6 mu (redshifted Ha). Our calibration includes on-ground and in-flight characterisation and monitoring of detector baseline, dark current, non-linearity, and sensitivity, to guarantee a relative photometric accuracy of better than 1.5%, and relative spectrophotometry to better than 0.7%. The wavelength calibration must be better than 5A. NISP is the state-of-the-art instrument in the NIR for all science beyond small areas available from HST and JWST - and an enormous advance due to its combination of field size and high throughput of telescope and instrument. During Euclid's 6-year survey covering 14000 deg^2 of extragalactic sky, NISP will be the backbone for determining distances of more than a billion galaxies. Its NIR data will become a rich reference imaging and spectroscopy data set for the coming decades., Comment: Paper submitted as part of the A&A special issue 'Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations
Published: 2024

10. Euclid. I. Overview of the Euclid mission

Author: Euclid Collaboration, Mellier, Y., Abdurro'uf, Barroso, J. A. Acevedo, Achúcarro, A., Adamek, J., Adam, R., Addison, G. E., Aghanim, N., Aguena, M., Ajani, V., Akrami, Y., Al-Bahlawan, A., Alavi, A., Albuquerque, I. S., Alestas, G., Alguero, G., Allaoui, A., Allen, S. W., Allevato, V., Alonso-Tetilla, A. V., Altieri, B., Alvarez-Candal, A., Amara, A., Amendola, L., Amiaux, J., Andika, I. T., Andreon, S., Andrews, A., Angora, G., Angulo, R. E., Annibali, F., Anselmi, A., Anselmi, S., Arcari, S., Archidiacono, M., Aricò, G., Arnaud, M., Arnouts, S., Asgari, M., Asorey, J., Atayde, L., Atek, H., Atrio-Barandela, F., Aubert, M., Aubourg, E., Auphan, T., Auricchio, N., Aussel, B., Aussel, H., Avelino, P. P., Avgoustidis, A., Avila, S., Awan, S., Azzollini, R., Baccigalupi, C., Bachelet, E., Bacon, D., Baes, M., Bagley, M. B., Bahr-Kalus, B., Balaguera-Antolinez, A., Balbinot, E., Balcells, M., Baldi, M., Baldry, I., Balestra, A., Ballardini, M., Ballester, O., Balogh, M., Bañados, E., Barbier, R., Bardelli, S., Barreiro, T., Barriere, J. -C., Barros, B. J., Barthelemy, A., Bartolo, N., Basset, A., Battaglia, P., Battisti, A. J., Baugh, C. M., Baumont, L., Bazzanini, L., Beaulieu, J. -P., Beckmann, V., Belikov, A. N., Bel, J., Bellagamba, F., Bella, M., Bellini, E., Benabed, K., Bender, R., Benevento, G., Bennett, C. L., Benson, K., Bergamini, P., Bermejo-Climent, J. R., Bernardeau, F., Bertacca, D., Berthe, M., Berthier, J., Bethermin, M., Beutler, F., Bevillon, C., Bhargava, S., Bhatawdekar, R., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Bolzonella, M., Bonchi, A., Bonici, M., Bonino, D., Bonino, L., Bonvin, C., Bon, W., Booth, J. T., Borgani, S., Borlaff, A. S., Borsato, E., Bose, B., Botticella, M. T., Boucaud, A., Bouche, F., Boucher, J. S., Boutigny, D., Bouvard, T., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brau-Nogue, S., Brekke, P., Bremer, M. N., Brescia, M., Breton, M. -A., Brinchmann, J., Brinckmann, T., Brockley-Blatt, C., Brodwin, M., Brouard, L., Brown, M. L., Bruton, S., Bucko, J., Buddelmeijer, H., Buenadicha, G., Buitrago, F., Burger, P., Burigana, C., Busillo, V., Busonero, D., Cabanac, R., Cabayol-Garcia, L., Cagliari, M. S., Caillat, A., Caillat, L., Calabrese, M., Calabro, A., Calderone, G., Calura, F., Quevedo, B. Camacho, Camera, S., Campos, L., Canas-Herrera, G., Candini, G. P., Cantiello, M., Capobianco, V., Cappellaro, E., Cappelluti, N., Cappi, A., Caputi, K. I., Cara, C., Carbone, C., Cardone, V. F., Carella, E., Carlberg, R. G., Carle, M., Carminati, L., Caro, F., Carrasco, J. M., Carretero, J., Carrilho, P., Duque, J. Carron, Carry, B., Carvalho, A., Carvalho, C. S., Casas, R., Casas, S., Casenove, P., Casey, C. M., Cassata, P., Castander, F. J., Castelao, D., Castellano, M., Castiblanco, L., Castignani, G., Castro, T., Cavet, C., Cavuoti, S., Chabaud, P. -Y., Chambers, K. C., Charles, Y., Charlot, S., Chartab, N., Chary, R., Chaumeil, F., Cho, H., Chon, G., Ciancetta, E., Ciliegi, P., Cimatti, A., Cimino, M., Cioni, M. -R. L., Claydon, R., Cleland, C., Clément, B., Clements, D. L., Clerc, N., Clesse, S., Codis, S., Cogato, F., Colbert, J., Cole, R. E., Coles, P., Collett, T. E., Collins, R. S., Colodro-Conde, C., Colombo, C., Combes, F., Conforti, V., Congedo, G., Conseil, S., Conselice, C. J., Contarini, S., Contini, T., Conversi, L., Cooray, A. R., Copin, Y., Corasaniti, P. -S., Corcho-Caballero, P., Corcione, L., Cordes, O., Corpace, O., Correnti, M., Costanzi, M., Costille, A., Courbin, F., Mifsud, L. Courcoult, Courtois, H. M., Cousinou, M. -C., Covone, G., Cowell, T., Cragg, C., Cresci, G., Cristiani, S., Crocce, M., Cropper, M., Crouzet, P. E, Csizi, B., Cuby, J. -G., Cucchetti, E., Cucciati, O., Cuillandre, J. -C., Cunha, P. A. C., Cuozzo, V., Daddi, E., D'Addona, M., Dafonte, C., Dagoneau, N., Dalessandro, E., Dalton, G. B., D'Amico, G., Dannerbauer, H., Danto, P., Das, I., Da Silva, A., da Silva, R., Daste, G., Davies, J. E., Davini, S., de Boer, T., Decarli, R., De Caro, B., Degaudenzi, H., Degni, G., de Jong, J. T. A., de la Bella, L. F., de la Torre, S., Delhaise, F., Delley, D., Delucchi, G., De Lucia, G., Denniston, J., De Paolis, F., De Petris, M., Derosa, A., Desai, S., Desjacques, V., Despali, G., Desprez, G., De Vicente-Albendea, J., Deville, Y., Dias, J. D. F., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Diego, J. M., Di Ferdinando, D., Di Giorgio, A. M., Dimauro, P., Dinis, J., Dolag, K., Dolding, C., Dole, H., Sánchez, H. Domínguez, Doré, O., Dournac, F., Douspis, M., Dreihahn, H., Droge, B., Dryer, B., Dubath, F., Duc, P. -A., Ducret, F., Duffy, C., Dufresne, F., Duncan, C. A. J., Dupac, X., Duret, V., Durrer, R., Durret, F., Dusini, S., Ealet, A., Eggemeier, A., Eisenhardt, P. R. M., Elbaz, D., Elkhashab, M. Y., Ellien, A., Endicott, J., Enia, A., Erben, T., Vigo, J. A. Escartin, Escoffier, S., Sanz, I. Escudero, Essert, J., Ettori, S., Ezziati, M., Fabbian, G., Fabricius, M., Fang, Y., Farina, A., Farina, M., Farinelli, R., Farrens, S., Faustini, F., Feltre, A., Ferguson, A. M. N., Ferrando, P., Ferrari, A. G., Ferré-Mateu, A., Ferreira, P. G., Ferreras, I., Ferrero, I., Ferriol, S., Ferruit, P., Filleul, D., Finelli, F., Finkelstein, S. L., Finoguenov, A., Fiorini, B., Flentge, F., Focardi, P., Fonseca, J., Fontana, A., Fontanot, F., Fornari, F., Fosalba, P., Fossati, M., Fotopoulou, S., Fouchez, D., Fourmanoit, N., Frailis, M., Fraix-Burnet, D., Franceschi, E., Franco, A., Franzetti, P., Freihoefer, J., Frittoli, G., Frugier, P. -A., Frusciante, N., Fumagalli, A., Fumagalli, M., Fumana, M., Fu, Y., Gabarra, L., Galeotta, S., Galluccio, L., Ganga, K., Gao, H., García-Bellido, J., Garcia, K., Gardner, J. P., Garilli, B., Gaspar-Venancio, L. -M., Gasparetto, T., Gautard, V., Gavazzi, R., Gaztanaga, E., Genolet, L., Santos, R. Genova, Gentile, F., George, K., Ghaffari, Z., Giacomini, F., Gianotti, F., Gibb, G. P. S., Gillard, W., Gillis, B., Ginolfi, M., Giocoli, C., Girardi, M., Giri, S. K., Goh, L. W. K., Gómez-Alvarez, P., Gonzalez, A. H., Gonzalez, E. J., Gonzalez, J. C., Beauchamps, S. Gouyou, Gozaliasl, G., Gracia-Carpio, J., Grandis, S., Granett, B. R., Granvik, M., Grazian, A., Gregorio, A., Grenet, C., Grillo, C., Grupp, F., Gruppioni, C., Gruppuso, A., Guerbuez, C., Guerrini, S., Guidi, M., Guillard, P., Gutierrez, C. M., Guttridge, P., Guzzo, L., Gwyn, S., Haapala, J., Haase, J., Haddow, C. R., Hailey, M., Hall, A., Hall, D., Hamaus, N., Haridasu, B. S., Harnois-Déraps, J., Harper, C., Hartley, W. G., Hasinger, G., Hassani, F., Hatch, N. A., Haugan, S. V. H., Häußler, B., Heavens, A., Heisenberg, L., Helmi, A., Helou, G., Hemmati, S., Henares, K., Herent, O., Hernández-Monteagudo, C., Heuberger, T., Hewett, P. C., Heydenreich, S., Hildebrandt, H., Hirschmann, M., Hjorth, J., Hoar, J., Hoekstra, H., Holland, A. D., Holliman, M. S., Holmes, W., Hook, I., Horeau, B., Hormuth, F., Hornstrup, A., Hosseini, S., Hu, D., Hudelot, P., Hudson, M. J., Huertas-Company, M., Huff, E. M., Hughes, A. C. N., Humphrey, A., Hunt, L. K., Huynh, D. D., Ibata, R., Ichikawa, K., Iglesias-Groth, S., Ilbert, O., Ilić, S., Ingoglia, L., Iodice, E., Israel, H., Israelsson, U. E., Izzo, L., Jablonka, P., Jackson, N., Jacobson, J., Jafariyazani, M., Jahnke, K., Jansen, H., Jarvis, M. J., Jasche, J., Jauzac, M., Jeffrey, N., Jhabvala, M., Jimenez-Teja, Y., Muñoz, A. Jimenez, Joachimi, B., Johansson, P. H., Joudaki, S., Jullo, E., Kajava, J. J. E., Kang, Y., Kannawadi, A., Kansal, V., Karagiannis, D., Kärcher, M., Kashlinsky, A., Kazandjian, M. V., Keck, F., Keihänen, E., Kerins, E., Kermiche, S., Khalil, A., Kiessling, A., Kiiveri, K., Kilbinger, M., Kim, J., King, R., Kirkpatrick, C. C., Kitching, T., Kluge, M., Knabenhans, M., Knapen, J. H., Knebe, A., Kneib, J. -P., Kohley, R., Koopmans, L. V. E., Koskinen, H., Koulouridis, E., Kou, R., Kovács, A., Kova{č}ić, I., Kowalczyk, A., Koyama, K., Kraljic, K., Krause, O., Kruk, S., Kubik, B., Kuchner, U., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lacasa, F., Lacey, C. G., La Franca, F., Lagarde, N., Lahav, O., Laigle, C., La Marca, A., La Marle, O., Lamine, B., Lam, M. C., Lançon, A., Landt, H., Langer, M., Lapi, A., Larcheveque, C., Larsen, S. S., Lattanzi, M., Laudisio, F., Laugier, D., Laureijs, R., Lavaux, G., Lawrenson, A., Lazanu, A., Lazeyras, T., Boulc'h, Q. Le, Brun, A. M. C. Le, Brun, V. Le, Leclercq, F., Lee, S., Graet, J. Le, Legrand, L., Leirvik, K. N., Jeune, M. Le, Lembo, M., Mignant, D. Le, Lepinzan, M. D., Lepori, F., Lesci, G. F., Lesgourgues, J., Leuzzi, L., Levi, M. E., Liaudat, T. I., Libet, G., Liebing, P., Ligori, S., Lilje, P. B., Lin, C. -C., Linde, D., Linder, E., Lindholm, V., Linke, L., Li, S. -S., Liu, S. J., Lloro, I., Lobo, F. S. N., Lodieu, N., Lombardi, M., Lombriser, L., Lonare, P., Longo, G., López-Caniego, M., Lopez, X. Lopez, Alvarez, J. Lorenzo, Loureiro, A., Loveday, J., Lusso, E., Macias-Perez, J., Maciaszek, T., Magliocchetti, M., Magnard, F., Magnier, E. A., Magro, A., Mahler, G., Mainetti, G., Maino, D., Maiorano, E., Malavasi, N., Mamon, G. A., Mancini, C., Mandelbaum, R., Manera, M., Manjón-García, A., Mannucci, F., Mansutti, O., Outeiro, M. Manteiga, Maoli, R., Maraston, C., Marcin, S., Marcos-Arenal, P., Margalef-Bentabol, B., Marggraf, O., Marinucci, D., Marinucci, M., Markovic, K., Marleau, F. R., Marpaud, J., Martignac, J., Martín-Fleitas, J., Martin-Moruno, P., Martin, E. L., Martinelli, M., Martinet, N., Martin, H., Martins, C. J. A. P., Marulli, F., Massari, D., Massey, R., Masters, D. C., Matarrese, S., Matsuoka, Y., Matthew, S., Maughan, B. J., Mauri, N., Maurin, L., Maurogordato, S., McCarthy, K., McConnachie, A. W., McCracken, H. J., McDonald, I., McEwen, J. D., McPartland, C. J. R., Medinaceli, E., Mehta, V., Mei, S., Melchior, M., Melin, J. -B., Ménard, B., Mendes, J., Mendez-Abreu, J., Meneghetti, M., Mercurio, A., Merlin, E., Metcalf, R. B., Meylan, G., Migliaccio, M., Mignoli, M., Miller, L., Miluzio, M., Milvang-Jensen, B., Mimoso, J. P., Miquel, R., Miyatake, H., Mobasher, B., Mohr, J. J., Monaco, P., Monguió, M., Montoro, A., Mora, A., Dizgah, A. Moradinezhad, Moresco, M., Moretti, C., Morgante, G., Morisset, N., Moriya, T. J., Morris, P. W., Mortlock, D. J., Moscardini, L., Mota, D. F., Moustakas, L. A., Moutard, T., Müller, T., Munari, E., Murphree, G., Murray, C., Murray, N., Musi, P., Nadathur, S., Nagam, B. C., Nagao, T., Naidoo, K., Nakajima, R., Nally, C., Natoli, P., Navarro-Alsina, A., Girones, D. Navarro, Neissner, C., Nersesian, A., Nesseris, S., Nguyen-Kim, H. N., Nicastro, L., Nichol, R. C., Nielbock, M., Niemi, S. -M., Nieto, S., Nilsson, K., Noller, J., Norberg, P., Nourizonoz, A., Ntelis, P., Nucita, A. A., Nugent, P., Nunes, N. J., Nutma, T., Ocampo, I., Odier, J., Oesch, P. A., Oguri, M., Oliveira, D. Magalhaes, Onoue, M., Oosterbroek, T., Oppizzi, F., Ordenovic, C., Osato, K., Pacaud, F., Pace, F., Padilla, C., Paech, K., Pagano, L., Page, M. J., Palazzi, E., Paltani, S., Pamuk, S., Pandolfi, S., Paoletti, D., Paolillo, M., Papaderos, P., Pardede, K., Parimbelli, G., Parmar, A., Partmann, C., Pasian, F., Passalacqua, F., Paterson, K., Patrizii, L., Pattison, C., Paulino-Afonso, A., Paviot, R., Peacock, J. A., Pearce, F. R., Pedersen, K., Peel, A., Peletier, R. F., Ibanez, M. Pellejero, Pello, R., Penny, M. T., Percival, W. J., Perez-Garrido, A., Perotto, L., Pettorino, V., Pezzotta, A., Pezzuto, S., Philippon, A., Piersanti, O., Pietroni, M., Piga, L., Pilo, L., Pires, S., Pisani, A., Pizzella, A., Pizzuti, L., Plana, C., Polenta, G., Pollack, J. E., Poncet, M., Pöntinen, M., Pool, P., Popa, L. A., Popa, V., Popp, J., Porciani, C., Porth, L., Potter, D., Poulain, M., Pourtsidou, A., Pozzetti, L., Prandoni, I., Pratt, G. W., Prezelus, S., Prieto, E., Pugno, A., Quai, S., Quilley, L., Racca, G. D., Raccanelli, A., Rácz, G., Radinović, S., Radovich, M., Ragagnin, A., Ragnit, U., Raison, F., Ramos-Chernenko, N., Ranc, C., Raylet, N., Rebolo, R., Refregier, A., Reimberg, P., Reiprich, T. H., Renk, F., Renzi, A., Retre, J., Revaz, Y., Reylé, C., Reynolds, L., Rhodes, J., Ricci, F., Ricci, M., Riccio, G., Ricken, S. O., Rissanen, S., Risso, I., Rix, H. -W., Robin, A. C., Rocca-Volmerange, B., Rocci, P. -F., Rodenhuis, M., Rodighiero, G., Monroy, M. Rodriguez, Rollins, R. P., Romanello, M., Roman, J., Romelli, E., Romero-Gomez, M., Roncarelli, M., Rosati, P., Rosset, C., Rossetti, E., Roster, W., Rottgering, H. J. A., Rozas-Fernández, A., Ruane, K., Rubino-Martin, J. A., Rudolph, A., Ruppin, F., Rusholme, B., Sacquegna, S., Sáez-Casares, I., Saga, S., Saglia, R., Sahlén, M., Saifollahi, T., Sakr, Z., Salvalaggio, J., Salvaterra, R., Salvati, L., Salvato, M., Salvignol, J. -C., Sánchez, A. G., Sanchez, E., Sanders, D. B., Sapone, D., Saponara, M., Sarpa, E., Sarron, F., Sartori, S., Sassolas, B., Sauniere, L., Sauvage, M., Sawicki, M., Scaramella, R., Scarlata, C., Scharré, L., Schaye, J., Schewtschenko, J. A., Schindler, J. -T., Schinnerer, E., Schirmer, M., Schmidt, F., Schmidt, M., Schneider, A., Schneider, M., Schneider, P., Schöneberg, N., Schrabback, T., Schultheis, M., Schulz, S., Schwartz, J., Sciotti, D., Scodeggio, M., Scognamiglio, D., Scott, D., Scottez, V., Secroun, A., Sefusatti, E., Seidel, G., Seiffert, M., Sellentin, E., Selwood, M., Semboloni, E., Sereno, M., Serjeant, S., Serrano, S., Shankar, F., Sharples, R. M., Short, A., Shulevski, A., Shuntov, M., Sias, M., Sikkema, G., Silvestri, A., Simon, P., Sirignano, C., Sirri, G., Skottfelt, J., Slezak, E., Sluse, D., Smith, G. P., Smith, L. C., Smith, R. E., Smit, S. J. A., Soldano, F., Solheim, B. G. B., Sorce, J. G., Sorrenti, F., Soubrie, E., Spinoglio, L., Mancini, A. Spurio, Stadel, J., Stagnaro, L., Stanco, L., Stanford, S. A., Starck, J. -L., Stassi, P., Steinwagner, J., Stern, D., Stone, C., Strada, P., Strafella, F., Stramaccioni, D., Surace, C., Sureau, F., Suyu, S. H., Swindells, I., Szafraniec, M., Szapudi, I., Taamoli, S., Talia, M., Tallada-Crespí, P., Tanidis, K., Tao, C., Tarrío, P., Tavagnacco, D., Taylor, A. N., Taylor, J. E., Taylor, P. L., Teixeira, E. M., Tenti, M., Idiago, P. Teodoro, Teplitz, H. I., Tereno, I., Tessore, N., Testa, V., Testera, G., Tewes, M., Teyssier, R., Theret, N., Thizy, C., Thomas, P. D., Toba, Y., Toft, S., Toledo-Moreo, R., Tolstoy, E., Tommasi, E., Torbaniuk, O., Torradeflot, F., Tortora, C., Tosi, S., Tosti, S., Trifoglio, M., Troja, A., Trombetti, T., Tronconi, A., Tsedrik, M., Tsyganov, A., Tucci, M., Tutusaus, I., Uhlemann, C., Ulivi, L., Urbano, M., Vacher, L., Vaillon, L., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Venemans, B., Venhola, A., Ventura, S., Kleijn, G. Verdoes, Vergani, D., Verma, A., Vernizzi, F., Veropalumbo, A., Verza, G., Vescovi, C., Vibert, D., Viel, M., Vielzeuf, P., Viglione, C., Viitanen, A., Villaescusa-Navarro, F., Vinciguerra, S., Visticot, F., Voggel, K., von Wietersheim-Kramsta, M., Vriend, W. J., Wachter, S., Walmsley, M., Walth, G., Walton, D. M., Walton, N. A., Wander, M., Wang, L., Wang, Y., Weaver, J. R., Weller, J., Whalen, D. J., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Youles, S., Yung, L. Y. A., Zacchei, A., Zalesky, L., Zamorani, G., Vitorelli, A. Zamorano, Marc, M. Zanoni, Zennaro, M., Zerbi, F. M., Zinchenko, I. A., Zoubian, J., Zucca, E., and Zumalacarregui, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Paper submitted as part of the A&A special issue`Euclid on Sky'
Published: 2024

11. Euclid. II. The VIS Instrument

Author: Euclid Collaboration, Cropper, M., Al-Bahlawan, A., Amiaux, J., Awan, S., Azzollini, R., Benson, K., Berthe, M., Boucher, J., Bozzo, E., Brockley-Blatt, C., Candini, G. P., Cara, C., Chaudery, R. A., Cole, R. E., Danto, P., Denniston, J., Di Giorgio, A. M., Dryer, B., Endicott, J., Dubois, J. -P., Farina, M., Galli, E., Genolet, L., Gow, J. P. D., Guttridge, P., Hailey, M., Hall, D., Harper, C., Holland, A. D., Horeau, B., Hu, D., King, R., James, R. E., Larcheveque, C., Khalil, A., Lawrenson, A., Liebing, P., Martignac, J., McCracken, H. J., Murray, N., Nakajima, R., Niemi, S. -M., Pendem, A., Paltani, S., Philippon, A., Pool, P., Plana, C., Pottinger, S., Racca, G. D., Rousseau, A., Ruane, K., Salatti, M., Salvignol, J. -C., Sciortino, A., Short, Alexander, Liu, S. J., Skottfelt, J., Swindells, I., Smit, S. J. A., Szafraniec, M., Thomas, P. D., Thomas, W., Tommasi, E., Winter, B., Tosti, S., Visticot, F., Walton, D. M., Willis, G., Mora, A., Kohley, R., Massey, R., Nightingale, J. W., Kitching, T., Hoekstra, H., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Aussel, H., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Bender, R., Bodendorf, C., Boenke, T., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, R., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cuby, J. -G., Cuillandre, J. -C., Da Silva, A., Degaudenzi, H., Dinis, J., Dolding, C., Douspis, M., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Fabricius, M., Farrens, S., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Frugier, P. -A., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Herent, O., Hoar, J., Holliman, M. S., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kilbinger, M., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Alvarez, J. Lorenzo, Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Martinet, N., Marulli, F., Masters, D. C., Maurogordato, S., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Miller, L., Mohr, J. J., Moresco, M., Moscardini, L., Nichol, R. C., Nutma, T., Padilla, C., Paech, K., Pasian, F., Peacock, J. A., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Refregier, A., Renzi, A., Riccio, G., Rix, Hans-Walter, Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Rottgering, H. J. A., Saglia, R., Sapone, D., Sauvage, M., Scaramella, R., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wachter, S., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Baccigalupi, C., Bernardeau, F., Biviano, A., Bolzonella, M., Boucaud, A., Burigana, C., Calabrese, M., Casenove, P., Colodro-Conde, C., Crocce, M., De Lucia, G., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Farinelli, R., Finelli, F., George, K., Gracia-Carpio, J., Ilić, S., Israel, H., Mainetti, G., Marcin, S., Martinelli, M., Mauri, N., Neissner, C., Nguyen-Kim, H. N., Pezzotta, A., Pöntinen, M., Porciani, C., Sakr, Z., Scottez, V., Sefusatti, E., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Aubourg, E., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Calabro, A., Calderone, G., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Chary, R., Contarini, S., Cooray, A. R., Cordes, O., Costanzi, M., Cucciati, O., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gregorio, A., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Huertas-Company, M., Ilbert, O., Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Lacasa, F., Graet, J. Le, Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., McPartland, C. J. R., Metcalf, R. B., Migliaccio, M., Miluzio, M., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Odier, J., Oguri, M., Patrizii, L., Popa, V., Potter, D., Pourtsidou, A., Reimberg, P., Risso, I., Rocci, P. -F., Rollins, R. P., Rusholme, B., Sahlén, M., Sánchez, A. G., Scarlata, C., Schaye, J., Schewtschenko, J. A., Schneider, A., Schultheis, M., Sereno, M., Shankar, F., Sikkema, G., Silvestri, A., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Steinwagner, J., Tanidis, K., Tao, C., Tessore, N., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Vernizzi, F., Verza, G., Vielzeuf, P., Weaver, J. R., Zalesky, L., Zinchenko, I. A., Archidiacono, M., Atrio-Barandela, F., Bouvard, T., Caro, F., Dimauro, P., Duc, P. -A., Fang, Y., Ferguson, A. M. N., Gasparetto, T., Gutierrez, C. M., Kova{č}ić, I., Kruk, S., Brun, A. M. C. Le, Liaudat, T. I., Montoro, A., Murray, C., Pagano, L., Paoletti, D., Sarpa, E., Viitanen, A., Lesgourgues, J., and Martín-Fleitas, J.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: This paper presents the specification, design, and development of the Visible Camera (VIS) on the ESA Euclid mission. VIS is a large optical-band imager with a field of view of 0.54 deg^2 sampled at 0.1" with an array of 609 Megapixels and spatial resolution of 0.18". It will be used to survey approximately 14,000 deg^2 of extragalactic sky to measure the distortion of galaxies in the redshift range z=0.1-1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes of Euclid. With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and, from how this has changed with look-back time, the nature of dark energy and theories of gravity can be constrained. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, reaching m_AB>24.5 with S/N >10 in a single broad I_E~(r+i+z) band over a six year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the VIS concept and describes the instrument design and development before reporting the pre-launch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than m_AB=25 with S/N>10 for galaxies of full-width half-maximum of 0.3" in a 1.3" diameter aperture over the Wide Survey, and m_AB>26.4 for a Deep Survey that will cover more than 50 deg^2. The paper also describes how VIS works with the other Euclid components of survey, telescope, and science data processing to extract the cosmological information., Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations
Published: 2024

12. Euclid preparation. 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 to deal with convolution by a point spread function 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. The scientific performance is quantified 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 point spread function with a full width at half maximum of $0.^{\!\prime\prime}2$ and spatial variation across the field of view. Objects are measured with a density of 90 arcmin$^{-2}$ ($I_{\rm E}<26.5$) in 4500 deg$^2$. The total shear bias is broken down into measurement (our main focus here) and selection effects (which will be addressed elsewhere). We find: 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: 29 pages, 18 figures, 2 tables
Published: 2024

13. Euclid preparation. Improving cosmological constraints using a new multi-tracer method with the spectroscopic and photometric samples

Author: Euclid Collaboration, Dournac, F., Blanchard, A., Ilić, S., Lamine, B., Tutusaus, I., Amara, A., Andreon, S., Auricchio, N., Aussel, H., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., 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., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Granett, B. R., 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., 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., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., 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., Schneider, P., Secroun, A., Seidel, G., Seiffert, M., Serrano, S., Sirignano, C., Sirri, G., Stanco, 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., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Colodro-Conde, C., De Lucia, G., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Mainetti, G., Martinelli, M., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Baccigalupi, C., Balaguera-Antolinez, A., Ballardini, M., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Coupon, J., Davini, S., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Munoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sánchez, A. G., Schewtschenko, J. A., Schneider, A., Sefusatti, E., Sereno, M., Steinwagner, J., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., Vergani, D., and Verza, G.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Future data provided by the Euclid mission will allow us to better understand the cosmic history of the Universe. A metric of its performance is the figure-of-merit (FoM) of dark energy, usually estimated with Fisher forecasts. The expected FoM has previously been estimated taking into account the two main probes of Euclid, namely the three-dimensional clustering of the spectroscopic galaxy sample, and the so-called 3x2pt signal from the photometric sample (i.e., the weak lensing signal, the galaxy clustering, and their cross-correlation). So far, these two probes have been treated as independent. In this paper, we introduce a new observable given by the ratio of the (angular) two-point correlation function of galaxies from the two surveys. For identical (normalised) selection functions, this observable is unaffected by sampling noise, and its variance is solely controlled by Poisson noise. We present forecasts for Euclid where this multi-tracer method is applied and is particularly relevant because the two surveys will cover the same area of the sky. This method allows for the exploitation of the combination of the spectroscopic and photometric samples. When the correlation between this new observable and the other probes is not taken into account, a significant gain is obtained in the FoM, as well as in the constraints on other cosmological parameters. The benefit is more pronounced for a commonly investigated modified gravity model, namely the $\gamma$ parametrisation of the growth factor. However, the correlation between the different probes is found to be significant and hence the actual gain is uncertain. We present various strategies for circumventing this issue and still extract useful information from the new observable., Comment: 16 pages, 7 figures, submitted to A&A
Published: 2024

14. 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

15. BEYONDPLANCK

Author: Svalheim, TL, Andersen, KJ, Aurlien, R, Banerji, R, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Paradiso, S, Partridge, B, Reinecke, M, Suur-Uski, A-S, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: Using the Planck Low Frequency Instrument (LFI) and WMAP data within the global Bayesian BEYONDPLANCK framework, we constrained the polarized foreground emission between 30 and 70 GHz. We combined, for the first time, full-resolution Planck LFI time-ordered data with low-resolution WMAP sky maps at 33, 40, and 61 GHz. The spectral parameters were fit with a likelihood defined at the native resolution of each frequency channel. This analysis represents the first implementation of true multi-resolution component separation applied to CMB observations for both amplitude and spectral energy distribution (SED) parameters. For the synchrotron emission, we approximated the SED as a power-law in frequency and we find that the low signal-to-noise ratio of the current data strongly limits the number of free parameters that can be robustly constrained. We partitioned the sky into four large disjoint regions (High Latitude; Galactic Spur; Galactic Plane; and Galactic Center), each associated with its own power-law index. We find that the High Latitude region is prior-dominated, while the Galactic Center region is contaminated by residual instrumental systematics. The two remaining regions appear to be signal-dominated, and for these we derive spectral indices of βsSpur=3.17±0.06 and βsPlane=3.03±0.07, which is in good agreement with previous results. For the thermal dust emission, we assumed a modified blackbody model and we fit a single power-law index across the full sky. We find βda =â 1.64±0.03, which is slightly steeper than the value reported in Planck HFI data, but still statistically consistent at the 2Ï confidence level.
Published: 2023

16. BEYONDPLANCK

Author: Galloway, M, Andersen, KJ, Aurlien, R, Banerji, R, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Mennella, A, Paradiso, S, Partridge, B, Reinecke, M, San, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Bioengineering, Genetics, Networking and Information Technology R&D (NITRD), Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We describe the computational infrastructure for end-to-end Bayesian cosmic microwave background (CMB) analysis implemented by the BeyondPlanck Collaboration. The code is called Commander3. It provides a statistically consistent framework for global analysis of CMB and microwave observations and may be useful for a wide range of legacy, current, and future experiments. The paper has three main goals. Firstly, we provide a high-level overview of the existing code base, aiming to guide readers who wish to extend and adapt the code according to their own needs or re-implement it from scratch in a different programming language. Secondly, we discuss some critical computational challenges that arise within any global CMB analysis framework, for instance in-memory compression of time-ordered data, fast Fourier transform optimization, and parallelization and load-balancing. Thirdly, we quantify the CPU and RAM requirements for the current BEYONDPLANCK analysis, finding that a total of 1.5 TB of RAM is required for efficient analysis and that the total cost of a full Gibbs sample for LFI is 170 CPU-hrs, including both low-level processing and high-level component separation, which is well within the capabilities of current low-cost computing facilities. The existing code base is made publicly available under a GNU General Public Library (GPL) license.
Published: 2023

17. BEYONDPLANCK

Author: Herman, D, Hensley, B, Andersen, KJ, Aurlien, R, Banerji, R, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Foss, MK, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Paradiso, S, Partridge, B, Reinecke, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Wehus, IK, and Zacchei, A
Subjects: Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We constrained the level of polarized anomalous microwave emission (AME) on large angular scales using Planck Low-Frequency Instrument (LFI) and WMAP polarization data within a Bayesian cosmic microwave background (CMB) analysis framework. We modeled synchrotron emission with a power-law spectral energy distribution, as well as the sum of AME and thermal dust emission through linear regression with the Planck High-Frequency Instrument (HFI) 353 GHz data. This template-based dust emission model allowed us to constrain the level of polarized AME while making minimal assumptions on its frequency dependence. We neglected CMB fluctuations, but show through simulations that these fluctuations have a minor impact on the results. We find that the resulting AME polarization fraction confidence limit is sensitive to the polarized synchrotron spectral index prior. In addition, for prior means βsâ
Published: 2023

18. BEYONDPLANCK

Author: Gjerløw, E, Ihle, HT, Galeotta, S, Andersen, KJ, Aurlien, R, Banerji, R, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Foss, MK, Franceschet, C, Fuskeland, U, Galloway, M, Gerakakis, S, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Paradiso, S, Partridge, B, Reinecke, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present a Bayesian calibration algorithm for cosmic microwave background (CMB) observations as implemented within the global end-to-end BEYONDPLANCK framework and applied to the Planck Low Frequency Instrument (LFI) data. Following the most recent Planck analysis, we decomposed the full time-dependent gain into a sum of three nearly orthogonal components: one absolute calibration term, common to all detectors, one time-independent term that can vary between detectors, and one time-dependent component that was allowed to vary between one-hour pointing periods. Each term was then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components were sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole-polarization quadrupole degeneracy and processing masks. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of 0.67% (0.84%) for 30 GHz, 0.12% (0.04%) for 44 GHz and 0.03% (0.64%) for 70 GHz, compared to Planck PR4 and Planck 2018, respectively. We note that the BEYONDPLANCK calibration was performed globally, which results in better inter-frequency consistency than previous estimates. Additionally, WMAP observations were used actively in the BEYONDPLANCK analysis, which both breaks internal degeneracies in the Planck data set and results in an overall better agreement with WMAP. Finally, we used a Wiener filtering approach to smoothing the gain estimates. We show that this method avoids artifacts in the correlated noise maps as a result of oversmoothing the gain solution, which is difficult to avoid with methods like boxcar smoothing, as Wiener filtering by construction maintains a balance between data fidelity and prior knowledge. Although our presentation and algorithm are currently oriented toward LFI processing, the general procedure is fully generalizable to other experiments, as long as the Solar dipole signal is available to be used for calibration.
Published: 2023

19. BEYONDPLANCK

Author: Keihänen, E, Suur-Uski, A-S, Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Mennella, A, Paradiso, S, Partridge, B, Reinecke, M, San, M, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present a Gibbs sampling solution to the mapmaking problem for cosmic microwave background (CMB) measurements that builds on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps: noise filtering and map binning. Considered as two separate steps, both are computationally much cheaper than solving the combined problem. This provides a huge performance benefit as compared to traditional methods and it allows us, for the first time, to bring the destriping baseline length to a single sample. Here, we applied the Gibbs procedure to simulated Planck 30 GHz data. We find that gaps in the time-ordered data are handled efficiently by filling them in with simulated noise as part of the Gibbs process. The Gibbs procedure yields a chain of map samples, from which we are able to compute the posterior mean as a best-estimate map. The variation in the chain provides information on the correlated residual noise, without the need to construct a full noise covariance matrix. However, if only a single maximum-likelihood frequency map estimate is required, we find that traditional conjugate gradient solvers converge much faster than a Gibbs sampler in terms of the total number of iterations. The conceptual advantages of the Gibbs sampling approach lies in statistically well-defined error propagation and systematic error correction. This methodology thus forms the conceptual basis for the mapmaking algorithm employed in the BEYONDPLANCK framework, which implements the first end-to-end Bayesian analysis pipeline for CMB observations.
Published: 2023

20. BEYONDPLANCK

Author: Basyrov, A, Suur-Uski, A-S, Colombo, LPL, Eskilt, JR, Paradiso, S, Andersen, KJ, Aurlien, R, Banerji, R, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Eriksen, HK, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Partridge, B, Reinecke, M, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present Planck Low Frequency Instrument (LFI) frequency sky maps derived within the BEYONDPLANCK framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical, and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples, each of which corresponds to one possible realization of the various modeled instrumental systematic corrections, including correlated noise, time-variable gain, as well as far sidelobe and bandpass corrections. This ensemble allows for computationally convenient end-to-end propagation of low-level instrumental uncertainties into higher-level science products, including astrophysical component maps, angular power spectra, and cosmological parameters. We show that the two dominant sources of LFI instrumental systematic uncertainties are correlated noise and gain fluctuations, and the products presented here support -for the first time -full Bayesian error propagation for these effects at full angular resolution. We compared our posterior mean maps with traditional frequency maps delivered by the Planck Collaboration, and find generally good agreement. The most important quality improvement is due to significantly lower calibration uncertainties in the new processing, as we find a fractional absolute calibration uncertainty at 70 GHz of δg0/g0â =â 5Ã - 105, which is nominally 40 times smaller than that reported by Planck 2018. However, we also note that the original Planck 2018 estimate has a nontrivial statistical interpretation, and this further illustrates the advantage of the new framework in terms of producing self-consistent and well-defined error estimates of all involved quantities without the need of ad hoc uncertainty contributions. We describe how low-resolution data products, including dense pixel-pixel covariance matrices, may be produced from the posterior samples directly, without the need for computationally expensive analytic calculations or simulations. We conclude that posterior-based frequency map sampling provides unique capabilities in terms of low-level systematics modeling and error propagation, and may play an important role for future Cosmic Microwave Background (CMB) B-mode experiments aiming at nanokelvin precision.
Published: 2023

21. BEYONDPLANCK

Author: Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Lunde, JGS, Maggio, G, Maino, D, Maris, M, Mennella, A, Paradiso, S, Partridge, B, Reinecke, M, San, M, Stutzer, N-O, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We describe the BEYONDPLANCK project in terms of our motivation, methodology, and main products, and provide a guide to a set of companion papers that describe each result in more detail. Building directly on experience from ESA's Planck mission, we implemented a complete end-to-end Bayesian analysis framework for the Planck Low Frequency Instrument (LFI) observations. The primary product is a full joint posterior distribution P(Ï â £d), where Ï represents the set of all free instrumental (gain, correlated noise, bandpass, etc.), astrophysical (synchrotron, free-free, thermal dust emission, etc.), and cosmological (cosmic microwave background -CMB -map, power spectrum, etc.) parameters. Some notable advantages of this approach compared to a traditional pipeline procedure are seamless end-to-end propagation of uncertainties; accurate modeling of both astrophysical and instrumental effects in the most natural basis for each uncertain quantity; optimized computational costs with little or no need for intermediate human interaction between various analysis steps; and a complete overview of the entire analysis process within one single framework. As a practical demonstration of this framework, we focus in particular on low-â CMB polarization reconstruction with Planck LFI. In this process, we identify several important new effects that have not been accounted for in previous pipelines, including gain over-smoothing and time-variable and non-1/f correlated noise in the 30 and 44 GHz channels. Modeling and mitigating both previously known and newly discovered systematic effects, we find that all results are consistent with the Î CDM model, and we constrained the reionization optical depth to Ï â =â 0.066±0.013, with a low-resolution CMB-based Ï 2 probability to exceed of 32%. This uncertainty is about 30% larger than the official pipelines, arising from taking a more complete instrumental model into account. The marginal CMB solar dipole amplitude is 3362.7±1.4â μK, where the error bar was derived directly from the posterior distribution without the need of any ad hoc instrumental corrections. We are currently not aware of any significant unmodeled systematic effects remaining in the Planck LFI data, and, for the first time, the 44 GHz channel is fully exploited in the current analysis. We argue that this framework can play a central role in the analysis of many current and future high-sensitivity CMB experiments, including LiteBIRD, and it will serve as the computational foundation of the emerging community-wide COSMOGLOBE effort, which aims to combine state-of-the-art radio, microwave, and submillimeter data sets into one global astrophysical model.
Published: 2023

22. From BEYONDPLANCK to COSMOGLOBE: Preliminary WMAP Q-band analysis

Author: Watts, DJ, Galloway, M, Ihle, HT, Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Lunde, JGS, Maggio, G, Maino, D, Maris, M, Paradiso, S, Partridge, B, Reinecke, M, San, M, Stutzer, N-O, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present the first application of the COSMOGLOBE analysis framework by analyzing nine-year WMAP time-ordered observations that uses similar machinery to that of BEYONDPLANCK for the Planck Low Frequency Instrument (LFI). We analyzed only the Q-band (41 GHz) data and report on the low-level analysis process based on uncalibrated time-ordered data to calibrated maps. Most of the existing BEYONDPLANCK pipeline may be reused for WMAP analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a Planck 44 GHz sample of 17 CPU-hrs; this demonstrates that a full end-to-end Bayesian processing of the WMAP data is computationally feasible. In general, our recovered maps are very similar to the maps released by the WMAP team, although with two notable differences. In terms of temperature, we find a ∼2â μK quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct 2.5â μK signal that has been previously referred to as poorly measured modes by the WMAP team. In the COSMOGLOBE processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modeling these poorly measured modes within the assumed parametric model by using Planck information to break the sky-synchronous degeneracies inherent in the WMAP scanning strategy.
Published: 2023

23. BEYONDPLANCK

Author: Colombo, LPL, Eskilt, JR, Paradiso, S, Thommesen, H, Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Eriksen, HK, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Partridge, B, Reinecke, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present posterior sample-based cosmic microwave background (CMB) constraints from Planck LFI and WMAP observations as derived through global end-to-end Bayesian processing within the BeyondPlanck framework. We first used these samples to study correlations between CMB, foreground, and instrumental parameters. We identified a particularly strong degeneracy between CMB temperature fluctuations and free-free emission on intermediate angular scales (400 ≤ ∫ ≤ 600), mitigated through model reduction, masking, and resampling. We compared our posterior-based CMB results with previous Planck products and found a generally good agreement, however, with notably higher noise due to our exclusion of Planck HFI data.We found a best-fit CMB dipole amplitude of 3362:7 ± 1:4 μK, which is in excellent agreement with previous Planck results. The quoted dipole uncertainty is derived directly from the sampled posterior distribution and does not involve any ad hoc contributions for Planck instrumental systematic effects. Similarly, we find a temperature quadrupole amplitude of φTT 2 = 229 ± 97 μK2, which is in good agreement with previous results in terms of the amplitude, but the uncertainty is one order of magnitude greater than the naive diagonal Fisher uncertainty. Concurrently, we find less evidence of a possible alignment between the quadrupole and octopole than previously reported, due to a much larger scatter in the individual quadrupole coeffcients that is caused both by marginalizing over a more complete set of systematic effects – as well as by requiring a more conservative analysis mask to mitigate the free-free degeneracy. For higher multipoles, we find that the angular temperature power spectrum is generally in good agreement with both Planck and WMAP. At the same time, we note that this is the first time that the sample-based, asymptotically exact Blackwell-Rao estimator has been successfully established for multipoles up to ∫ ≤ 600. It now accounts for the majority of the cosmologically important information. Overall, this analysis demonstrates the unique capabilities of the Bayesian approach with respect to end-to-end systematic uncertainty propagation and we believe it can and should play an important role in the analysis of future CMB experiments. Cosmological parameter constraints are presented in a companion paper.
Published: 2023

24. BEYONDPLANCK

Author: Brilenkov, M, Fornazier, KSF, Hergt, LT, Hoerning, GA, Marins, A, Murokoshi, T, Rahman, F, Stutzer, N-O, Zhou, Y, Abdalla, FB, Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Battista, A, Bersanelli, M, Bertocco, S, Bollanos, S, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Hoang, TD, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Paradiso, S, Partridge, B, Reinecke, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Tomasi, M, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, cosmic background radiation, cosmology, observations, diffuse radiation, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: End-to-end simulations play a key role in the analysis of any high-sensitivity cosmic microwave background (CMB) experiment, providing high-fidelity systematic error propagation capabilities that are unmatched by any other means. In this paper, we address an important issue regarding such simulations, namely, how to define the inputs in terms of sky model and instrument parameters. These may either be taken as a constrained realization derived from the data or as a random realization independent from the data. We refer to these as posterior and prior simulations, respectively. We show that the two options lead to significantly different correlation structures, as prior simulations (contrary to posterior simulations) effectively include cosmic variance, but they exclude realization-specific correlations from non-linear degeneracies. Consequently, they quantify fundamentally different types of uncertainties. We argue that as a result, they also have different and complementary scientific uses, even if this dichotomy is not absolute. In particular, posterior simulations are in general more convenient for parameter estimation studies, while prior simulations are generally more convenient for model testing. Before BEYONDPLANCK, most pipelines used a mix of constrained and random inputs and applied the same hybrid simulations for all applications, even though the statistical justification for this is not always evident. BEYONDPLANCK represents the first end-to-end CMB simulation framework that is able to generate both types of simulations and these new capabilities have brought this topic to the forefront. The BEYONDPLANCK posterior simulations and their uses are described extensively in a suite of companion papers. In this work, we consider one important applications of the corresponding prior simulations, namely, code validation. Specifically, we generated a set of one-year LFI 30 GHz prior simulations with known inputs and we used these to validate the core low-level BEYONDPLANCK algorithms dealing with gain estimation, correlated noise estimation, and mapmaking.
Published: 2023

25. BEYONDPLANCK

Author: Paradiso, S, Colombo, LPL, Andersen, KJ, Aurlien, R, Banerji, R, Basyrov, A, Bersanelli, M, Bertocco, S, Brilenkov, M, Carbone, M, Eriksen, HK, Eskilt, JR, Foss, MK, Franceschet, C, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Gjerløw, E, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Ihle, HT, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Partridge, B, Reinecke, M, San, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present cosmological parameter constraints estimated using the Bayesian BeyondPlanck analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data onto final cosmological parameters. As a first demonstration of the method, we analyzed time-ordered Planck LFI observations, combined with selected external data (WMAP 33–61 GHz, Planck HFI DR4 353 and 857 GHz, and Haslam 408 MHz) in the form of pixelized maps that are used to break critical astrophysical degeneracies. Overall, all the results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter amounting about 1φ when considering only temperature multipoles between 30 ≤ ∫ ≤ 600. In cases where there are differences, we note that the BeyondPlanck results are generally slightly closer to the high- ∫ HFI-dominated Planck 2018 results than previous analyses, suggesting slightly less tension between low and high multipoles. Using low- ∫ polarization information from LFI and WMAP, we find a best-fit value of φ = 0:066±0:013, which is higher than the low value of φ = 0:052 ± 0:008 derived from Planck 2018 and slightly lower than the value of 0:069 ± 0:011 derived from the joint analysis of offcial LFI and WMAP products. Most importantly, however, we find that the uncertainty derived in the BeyondPlanck processing is about 30% greater than when analyzing the offcial products, after taking into account the different sky coverage. We argue that this uncertainty is due to a marginalization over a more complete model of instrumental and astrophysical parameters, which results in more reliable and more rigorously defined uncertainties. We find that about 2000 Monte Carlo samples are required to achieve a robust convergence for a low-resolution cosmic microwave background (CMB) covariance matrix with 225 independent modes, and producing these samples takes about eight weeks on a modest computing cluster with 256 cores.
Published: 2023

26. BEYONDPLANCK

Author: Ihle, HT, Bersanelli, M, Franceschet, C, Gjerløw, E, Andersen, KJ, Aurlien, R, Banerji, R, Bertocco, S, Brilenkov, M, Carbone, M, Colombo, LPL, Eriksen, HK, Eskilt, JR, Foss, MK, Fuskeland, U, Galeotta, S, Galloway, M, Gerakakis, S, Hensley, B, Herman, D, Iacobellis, M, Ieronymaki, M, Jewell, JB, Karakci, A, Keihänen, E, Keskitalo, R, Maggio, G, Maino, D, Maris, M, Mennella, A, Paradiso, S, Partridge, B, Reinecke, M, San, M, Suur-Uski, A-S, Svalheim, TL, Tavagnacco, D, Thommesen, H, Watts, DJ, Wehus, IK, and Zacchei, A
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We present a Bayesian method for estimating instrumental noise parameters and propagating noise uncertainties within the global BEYONDPLANCK Gibbs sampling framework, which we applied to Planck Low Frequency Instrument (LFI) time-ordered data. Following previous works in the literature, we initially adopted a 1/f model for the noise power spectral density (PSD), but we found the need for an additional lognormal component in the noise model in the 30 and 44 GHz bands. We implemented an optimal Wiener-filter (or constrained realization) gap-filling procedure to account for masked data. We then used this procedure to both estimate the gapless correlated noise in the time-domain, ncorr, and to sample the noise PSD parameters, ξnâ =â {Ï0,â fknee,â α,â Ap}. In contrast to previous Planck analyses, we assumed piecewise stationary noise only within each pointing period (PID), and not throughout the full mission, but we adopted the LFI Data Processing Center results as priors on α and fknee. We generally found best-fit correlated noise parameters that are mostly consistent with previous results, with a few notable exceptions. However, a detailed inspection of the time-dependent results has revealed many important findings. First and foremost, we find strong evidence for statistically significant temporal variations in all noise PSD parameters, many of which are directly correlated with satellite housekeeping data. Second, while the simple 1/f model appears to be an excellent fit for the LFI 70 GHz channel, there is evidence for additional correlated noise that is not described by a 1/f model in the 30 and 44 GHz channels, including within the primary science frequency range of 0.1-1 Hz. In general, most 30 and 44 GHz channels exhibit deviations from 1/f at the 2-3Ï level in each one-hour pointing period, motivating the addition of the lognormal noise component for these bands. For certain periods of time, we also find evidence of strong common mode noise fluctuations across the entire focal plane. Overall, we conclude that a simple 1/f profile is not adequate for obtaining a full characterization of the Planck LFI noise, even when fitted hour-by-hour, and a more general model is required. These findings have important implications for large-scale CMB polarization reconstruction with the Planck LFI data and the current work is a first attempt at understanding and mitigating these issues.
Published: 2023

27. Euclid preparation. XXX. Performance assessment of the NISP Red-Grism through spectroscopic simulations for the Wide and Deep surveys

Author: Euclid Collaboration, Gabarra, L., Mancini, C., Munoz, L. Rodriguez, Rodighiero, G., Sirignano, C., Scodeggio, M., Talia, M., Dusini, S., Gillard, W., Granett, B. R., Maiorano, E., Moresco, M., Paganin, L., Palazzi, E., Pozzetti, L., Renzi, A., Rossetti, E., Vergani, D., Allevato, V., Bisigello, L., Castignani, G., De Caro, B., Fumana, M., Ganga, K., Garilli, B., Hirschmann, M., La Franca, F., Laigle, C., Passalacqua, F., Schirmer, M., Stanco, L., Troja, A., Yung, L. Y. A., Zamorani, G., Zoubian, J., Aghanim, N., 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., Castander, F. J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Ealet, A., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Holmes, W., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Mei, S., Meneghetti, M., Meylan, G., Moscardini, L., Munari, E., Nichol, R. C., Niemi, S. -M., Nightingale, J., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Raison, F., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirri, G., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Trifoglio, M., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zacchei, A., Andreon, S., Aussel, H., Bardelli, S., Bolzonella, M., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farina, M., Graciá-Carpio, J., Keihänen, E., Lindholm, V., Maino, D., Mauri, N., Mellier, Y., Neissner, C., Scottez, V., Tenti, M., Zucca, E., Akrami, Y., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borlaff, A. S., Borsato, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castro, T., Chambers, K., Cooray, A. R., Coupon, J., Courtois, H. M., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Ferrero, I., Finelli, F., Fotopoulou, S., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., 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., Marcin, S., Martinelli, M., Martinet, N., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Sánchez, A. G., Sakr, Z., Schneider, A., Sefusatti, E., Sereno, M., Shulevski, A., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Teyssier, R., Valiviita, J., Veropalumbo, A., Viel, M., and Zinchenko, I. A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: This work focuses on the pilot run of a simulation campaign aimed at investigating the spectroscopic capabilities of the Euclid Near-Infrared Spectrometer and Photometer (NISP), in terms of continuum and emission line detection in the context of galaxy evolutionary studies. To this purpose we constructed, emulated, and analysed the spectra of 4992 star-forming galaxies at $0.3 \leq z \leq 2.5$ using the NISP pixel-level simulator. We built the spectral library starting from public multi-wavelength galaxy catalogues, with value-added information on spectral energy distribution (SED) fitting results, and from Bruzual and Charlot (2003) stellar population templates. Rest-frame optical and near-IR nebular emission lines were included using empirical and theoretical relations. We inferred the 3.5$\sigma$ NISP red grism spectroscopic detection limit of the continuum measured in the $H$ band for star-forming galaxies with a median disk half-light radius of \ang{;;0.4} at magnitude $H= 19.5\pm0.2\,$AB$\,$mag for the Euclid Wide Survey and at $H = 20.8\pm0.6\,$AB$\,$mag for the Euclid Deep Survey. We found a very good agreement with the red grism emission line detection limit requirement for the Wide and Deep surveys. We characterised the effect of the galaxy shape on the detection capability of the red grism and highlighted the degradation of the quality of the extracted spectra as the disk size increases. In particular, we found that the extracted emission line signal to noise ratio (SNR) drops by $\sim\,$45$\%$ when the disk size ranges from \ang{;;0.25} to \ang{;;1}. These trends lead to a correlation between the emission line SNR and the stellar mass of the galaxy and we demonstrate the effect in a stacking analysis unveiling emission lines otherwise too faint to detect., Comment: 24 pages, 21 figures
Published: 2023
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28. 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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29. BeyondPlanck IV. On end-to-end simulations in CMB analysis -- Bayesian versus frequentist statistics

Author: Brilenkov, M., Fornazier, K. S. F., Hergt, L. T., Hoerning, G. A., Marins, A., Murokoshi, T., Rahman, F., Stutzer, N. -O., Zhou, Y., Abdalla, F. B., Andersen, K. J., Aurlien, R., Banerji, R., Basyrov, A., Battista, A., Bersanelli, M., Bertocco, S., Bollanos, S., Colombo, L. P. L., Eriksen, H. K., Eskilt, J. R., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerlow, E., Hensley, B., Herman, D., Hoang, T. D., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihanen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Tomasi, M., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: End-to-end simulations play a key role in the analysis of any high-sensitivity CMB experiment, providing high-fidelity systematic error propagation capabilities unmatched by any other means. In this paper, we address an important issue regarding such simulations, namely how to define the inputs in terms of sky model and instrument parameters. These may either be taken as a constrained realization derived from the data, or as a random realization independent from the data. We refer to these as Bayesian and frequentist simulations, respectively. We show that the two options lead to significantly different correlation structures, as frequentist simulations, contrary to Bayesian simulations, effectively include cosmic variance, but exclude realization-specific correlations from non-linear degeneracies. Consequently, they quantify fundamentally different types of uncertainties, and we argue that they therefore also have different and complementary scientific uses, even if this dichotomy is not absolute. Before BeyondPlanck, most pipelines have used a mix of constrained and random inputs, and used the same hybrid simulations for all applications, even though the statistical justification for this is not always evident. BeyondPlanck represents the first end-to-end CMB simulation framework that is able to generate both types of simulations, and these new capabilities have brought this topic to the forefront. The Bayesian BeyondPlanck simulations and their uses are described extensively in a suite of companion papers. In this paper we consider one important applications of the corresponding frequentist simulations, namely code validation. That is, we generate a set of 1-year LFI 30 GHz frequentist simulations with known inputs, and use these to validate the core low-level BeyondPlanck algorithms; gain estimation, correlated noise estimation, and mapmaking.
Published: 2022
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30. BeyondPlanck X. Planck LFI frequency maps with sample-based error propagation

Author: Basyrov, A., Suur-Uski, A. -S., Colombo, L. P. L., Eskilt, J. R., Paradiso, S., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Partridge, B., Reinecke, M., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present Planck LFI frequency sky maps derived within the BeyondPlanck framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples. This ensemble allows for computationally convenient end-to-end propagation of low-level instrumental uncertainties into higher-level science products. We show that the two dominant sources of LFI instrumental systematic uncertainties are correlated noise and gain fluctuations, and the products presented here support - for the first time - full Bayesian error propagation for these effects at full angular resolution. We compare our posterior mean maps with traditional frequency maps delivered by the Planck collaboration, and find generally good agreement. The most important quality improvement is due to significantly lower calibration uncertainties in the new processing, as we find a fractional absolute calibration uncertainty at 70 GHz of $\delta g_{0}/g_{0} =5 \cdot 10^{-5}$, which is nominally 40 times smaller than that reported by Planck 2018. However, the original Planck 2018 estimate has a non-trivial statistical interpretation, and this further illustrates the advantage of the new framework in terms of producing self-consistent and well-defined error estimates of all involved quantities without the need of ad hoc uncertainty contributions. We describe how low-resolution data products, including dense pixel-pixel covariance matrices, may be produced directly from the posterior samples without the need for computationally expensive analytic calculations or simulations. We conclude that posterior-based frequency map sampling provides unique capabilities in terms of low-level systematics modelling and error propagation, and may play an important role for future CMB B-mode experiments. (Abridged.), Comment: 32 pages, 23 figures, data available from https://www.cosmoglobe.uio.no/
Published: 2022
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31. BeyondPlanck XI. Bayesian CMB analysis with sample-based end-to-end error propagation

Author: Colombo, L. P. L., Eskilt, J. R., Paradiso, S., Thommesen, H., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present posterior sample-based cosmic microwave background (CMB) constraints from Planck LFI and WMAP observations derived through global end-to-end Bayesian processing. We use these samples to study correlations between CMB, foreground, and instrumental parameters, and we identify a particularly strong degeneracy between CMB temperature fluctuations and free-free emission on intermediate angular scales, which is mitigated through model reduction, masking, and resampling. We compare our posterior-based CMB results with previous Planck products, and find generally good agreement, but with higher noise due to exclusion of HFI data. We find a best-fit CMB dipole amplitude of $3362.7\pm1.4{\mu}K$, in excellent agreement with previous Planck results. The quoted uncertainty is derived directly from the sampled posterior distribution, and does not involve any ad hoc contribution for systematic effects. Similarly, we find a temperature quadrupole amplitude of $\sigma^{TT}_2=229\pm97{\mu}K^2$, in good agreement with previous results in terms of the amplitude, but the uncertainty is an order of magnitude larger than the diagonal Fisher uncertainty. Relatedly, we find lower evidence for a possible alignment between $\ell = 2$ and $\ell = 3$ than previously reported due to a much larger scatter in the individual quadrupole coefficients, caused both by marginalizing over a more complete set of systematic effects, and by our more conservative analysis mask. For higher multipoles, we find that the angular temperature power spectrum is generally in good agreement with both Planck and WMAP. This is the first time the sample-based asymptotically exact Blackwell-Rao estimator has been successfully established for multipoles up to $\ell\le600$, and it now accounts for the majority of the cosmologically important information. Cosmological parameter constraints are presented in a companion paper. (Abriged), Comment: 26 pages, 24 figures. Submitted to A&A. Part of the BeyondPlanck paper suite
Published: 2022
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32. Euclid preparation: XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

Author: Euclid Collaboration, Bisigello, L., Conselice, C. J., Baes, M., Bolzonella, M., Brescia, M., Cavuoti, S., Cucciati, O., Humphrey, A., Hunt, L. K., Maraston, C., Pozzetti, L., Tortora, C., van Mierlo, S. E., Aghanim, N., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cimatti, A., Congedo, G., 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., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., 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., 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., Polenta, G., Poncet, M., Popa, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rix, H. -W., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Boucaud, S. Bardelli A., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Maino, D., Mei, S., Scottez, V., Sureau, F., Tenti, M., Zucca, E., Borlaff, A. S., Ballardini, M., Biviano, A., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Cuby, J., Davini, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Huertas-Company, M., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macías-Pérez, J. F., Magliocchetti, M., Mainetti, G., Marcin, S., Martinelli, M., Martinet, N., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., and Viel, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and Convolutional Neutral Networks (CNN), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multi-band magnitudes together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving i) the redshift within a normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning., Comment: accepted for publication in MNRAS, 21 pages, 22 figures, 6 tables
Published: 2022

33. Euclid: Forecasts from the void-lensing cross-correlation

Author: Bonici, M., Carbone, C., Davini, S., Vielzeuf, P., Paganin, L., Cardone, V., Hamaus, N., Pisani, A., Hawken, A. J., Kovacs, A., Nadathur, S., Contarini, S., Verza, G., Tutusaus, I., Marulli, F., Moscardini, L., Aubert, M., Giocoli, C., Pourtsidou, A., Camera, S., Escoffier, S., Caminata, A., Martinelli, M., Pallavicini, M., Pettorino, V., Sakr, Z., Sapone, D., Testera, G., Tosi, S., Yankelevich, V., Amara, A., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., 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., Ealet, A., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Gomez-Alvarez, P., Garilli, B., Gillis, B., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kummel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Medinaceli, E., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pires, S., Polenta, G., Poncet, M., Popa, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Rossetti, E., Saglia, R., Sartoris, B., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Surace, C., Tallada-Crespi, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., and Andreon, S.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Euclid space telescope will survey a large dataset of cosmic voids traced by dense samples of galaxies. In this work we estimate its expected performance when exploiting angular photometric void clustering, galaxy weak lensing and their cross-correlation. To this aim, we implement a Fisher matrix approach tailored for voids from the Euclid photometric dataset and present the first forecasts on cosmological parameters that include the void-lensing correlation. We examine two different probe settings, pessimistic and optimistic, both for void clustering and galaxy lensing. We carry out forecast analyses in four model cosmologies, accounting for a varying total neutrino mass, $M_\nu$, and a dynamical dark energy (DE) equation of state, $w(z)$, described by the CPL parametrisation. We find that void clustering constraints on $h$ and $\Omega_b$ are competitive with galaxy lensing alone, while errors on $n_s$ decrease thanks to the orthogonality of the two probes in the 2D-projected parameter space. We also note that, as a whole, the inclusion of the void-lensing cross-correlation signal improves parameter constraints by $10-15\%$, and enhances the joint void clustering and galaxy lensing Figure of Merit (FoM) by $10\%$ and $25\%$, in the pessimistic and optimistic scenarios, respectively. Finally, when further combining with the spectroscopic galaxy clustering, assumed as an independent probe, we find that, in the most competitive case, the FoM increases by a factor of 4 with respect to the combination of weak lensing and spectroscopic galaxy clustering taken as independent probes. The forecasts presented in this work show that photometric void-clustering and its cross-correlation with galaxy lensing deserve to be exploited in the data analysis of the Euclid galaxy survey and promise to improve its constraining power, especially on $h$, $\Omega_b$, the neutrino mass, and the DE evolution., Comment: 22 pages, 9 figures - published in A&A
Published: 2022
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34. Euclid preparation: XX. The Complete Calibration of the Color-Redshift Relation survey: LBT observations and data release

Author: Euclid Collaboration, Saglia, R., De Nicola, S., Fabricius, M., Guglielmo, V., Snigula, J., Zöller, R., Bender, R., Heidt, J., Masters, D., Stern, D., Paltani, S., Amara, A., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, 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., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J., Melchior, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Graciá-Carpio, J., Maino, D., Mauri, N., Tramacere, A., Zucca, E., Ayllon, A. Alvarez, Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Davini, S., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macías-Pérez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martinet, N., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Veropalumbo, A., and Viel, M.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Complete Calibration of the Color-Redshift Relation survey (C3R2) is a spectroscopic programme designed to empirically calibrate the galaxy color-redshift relation to the Euclid depth (I_E=24.5), a key ingredient for the success of Stage IV dark energy projects based on weak lensing cosmology. A spectroscopic calibration sample as representative as possible of the galaxies in the Euclid weak lensing sample is being collected, selecting galaxies from a self-organizing map (SOM) representation of the galaxy color space. Here, we present the results of a near-infrared H- and K-bands spectroscopic campaign carried out using the LUCI instruments at the LBT. For a total of 251 galaxies, we present new highly-reliable redshifts in the 1.3<= z <=1.7 and 2<= z<=2.7 ranges. The newly-determined redshifts populate 49 SOM cells which previously contained no spectroscopic measurements and almost double the occupation numbers of an additional 153 SOM cells. A final optical ground-based observational effort is needed to calibrate the missing cells in particular in the redshift range 1.7<= z<=2.7 that lack spectroscopic calibration. In the end, Euclid itself will deliver telluric-free NIR spectra that can complete the calibration., Comment: A&A language edited version
Published: 2022
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35. Euclid: Fast two-point correlation function covariance through linear construction

Author: Keihanen, E., Lindholm, V., Monaco, P., Blot, L., Carbone, C., Kiiveri, K., Sánchez, A. G., Viitanen, A., Valiviita, J., Amara, A., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kitching, T., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Morin, B., 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., Raison, F., Renzi, A., Rhodes, J., Romelli, E., Saglia, R., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, 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., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Maino, D., and de la Torre, S.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present a method for fast evaluation of the covariance matrix for a two-point galaxy correlation function (2PCF) measured with the Landy-Szalay estimator. The standard way of evaluating the covariance matrix consists in running the estimator on a large number of mock catalogs, and evaluating their sample covariance. With large random catalog sizes (data-to-random objects ratio M>>1) the computational cost of the standard method is dominated by that of counting the data-random and random-random pairs, while the uncertainty of the estimate is dominated by that of data-data pairs. We present a method called Linear Construction (LC), where the covariance is estimated for small random catalogs of size M = 1 and M = 2, and the covariance for arbitrary M is constructed as a linear combination of these. We validate the method with PINOCCHIO simulations in range r = 20-200 Mpc/h, and show that the covariance estimate is unbiased. With M = 50 and with 2 Mpc/h bins, the theoretical speed-up of the method is a factor of 14. We discuss the impact on the precision matrix and parameter estimation, and derive a formula for the covariance of covariance., Comment: 17 pages, 11 figures
Published: 2022
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36. From BeyondPlanck to Cosmoglobe: Open Science, Reproducibility, and Data Longevity

Author: Gerakakis, S., Brilenkov, M., Ieronymaki, M., San, M., Watts, D. J., Andersen, K. J., Aurlien, R., Banerji, R., Basyrov, A., Bersanelli, M., Bertocco, S., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Eskilt, J. R., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Lunde, J. G. S., Maggio, G., Maino, D., Maris, M., Paradiso, S., Reinecke, M., Stutzer, N. -O., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The BeyondPlanck and Cosmoglobe collaborations have implemented the first integrated Bayesian end-to-end analysis pipeline for CMB experiments. The primary long-term motivation for this work is to develop a common analysis platform that supports efficient global joint analysis of complementary radio, microwave, and sub-millimeter experiments. A strict prerequisite for this to succeed is broad participation from the CMB community, and two foundational aspects of the program are therefore reproducibility and Open Science. In this paper, we discuss our efforts toward this aim. We also discuss measures toward facilitating easy code and data distribution, community-based code documentation, user-friendly compilation procedures, etc. This work represents the first publicly released end-to-end CMB analysis pipeline that includes raw data, source code, parameter files, and documentation. We argue that such a complete pipeline release should be a requirement for all major future and publicly-funded CMB experiments, noting that a full public release significantly increases data longevity by ensuring that the data quality can be improved whenever better processing techniques, complementary datasets, or more computing power become available, and thereby also taxpayers' value for money; providing only raw data and final products is not sufficient to guarantee full reproducibility in the future.
Published: 2022

37. BeyondPlanck XII. Cosmological parameter constraints with end-to-end error propagation

Author: Paradiso, S., Colombo, L. P. L., Andersen, K. J., Aurlien, R., Banerji, R., Basyrov, A., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Eriksen, H. K., Eskilt, J. R., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Partridge, B., Reinecke, M., San, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, 83F05
Abstract: We present cosmological parameter constraints as estimated using the Bayesian BeyondPlanck (BP) analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data to final cosmological parameters. As a first demonstration of the method, we analyze time-ordered Planck LFI observations, combined with selected external data (WMAP 33-61GHz, Planck HFI DR4 353 and 857GHz, and Haslam 408MHz) in the form of pixelized maps which are used to break critical astrophysical degeneracies. Overall, all results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter of about 1 sigma when considering only temperature multipoles between 29
Published: 2022
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38. BeyondPlanck V. Minimal ADC Corrections for Planck LFI

Author: Herman, D., Watson, R. A., Andersen, K. J., Aurlien, R., Banjeri, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Maggio, G., Maino, D., Maris, M., Mennella, A., Paradiso, S., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We describe the correction procedure for Analog-to-Digital Converter (ADC) differential non-linearities (DNL) adopted in the Bayesian end-to-end BeyondPlanck analysis framework. This method is nearly identical to that developed for the official LFI Data Processing Center (DPC) analysis, and relies on the binned rms noise profile of each detector data stream. However, rather than building the correction profile directly from the raw rms profile, we first fit a Gaussian to each significant ADC-induced rms decrement, and then derive the corresponding correction model from this smooth model. The main advange of this approach is that only samples which are significantly affected by ADC DNLs are corrected. The new corrections are only applied to data for which there is a clear detection of the non-linearities, and for which they perform at least comparably with the DPC corrections. Out of a total of 88 LFI data streams (sky and reference load for each of the 44 detectors) we apply the new minimal ADC corrections in 25 cases, and maintain the DPC corrections in 8 cases. All these correctsion are applited to 44 or 70 GHz channels, while, as in previous analyses, none of the 30 GHz ADCs show significant evidence of non-linearity. By comparing the BeyondPlanck and DPC ADC correction methods, we estimate that the residual ADC uncertainty is about two orders of magnitude below the total noise of both the 44 and 70 GHz channels, and their impact on current cosmological parameter estimation is small. However, we also show that non-idealities in the ADC corrections can generate sharp stripes in the final frequency maps, and these could be important for future joint analyses with HFI, WMAP, or other datasets. We therefore conclude that, although the existing corrections are adequate for LFI-based cosmological parameter analysis, further work on LFI ADC corrections is still warranted., Comment: 9 pages, 8 figures, mild language edits
Published: 2022
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39. From BeyondPlanck to Cosmoglobe: Preliminary $\mathit{WMAP}$ $\mathit Q$-band analysis

Author: Watts, D. J., Galloway, M., Ihle, H. T., Andersen, K. J., Aurlien, R., Banerji, R., Basyrov, A., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Eskilt, J. R., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Lunde, J. G. S., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Reinecke, M., San, M., Stutzer, N. O., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present the first application of the Cosmoglobe analysis framework by analyzing 9-year $\mathit{WMAP}$ time-ordered observations using similar machinery as BeyondPlanck utilizes for $\mathit{Planck}$ LFI. We analyze only the $\mathit Q$-band (41 GHz) data and report on the low-level analysis process from uncalibrated time-ordered data to calibrated maps. Most of the existing BeyondPlanck pipeline may be reused for $\mathit{WMAP}$ analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the $\mathit{WMAP}$ team. Producing a single $\mathit{WMAP}$ $\mathit Q$1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a $\mathit{Planck}$ 44 GHz sample of 17 CPU-hrs; this demonstrates that full end-to-end Bayesian processing of the $\mathit{WMAP}$ data is computationally feasible. In general, our recovered maps are very similar to the maps released by the $\mathit{WMAP}$ team, although with two notable differences. In temperature we find a $\sim2\,\mathrm{\mu K}$ quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct $2.5\,\mathrm{\mu K}$ signal that has been previously called poorly-measured modes by the $\mathit{WMAP}$ team. In the Cosmoglobe processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modelling these poorly measured modes within the assumed parametric model by using $\mathit{Planck}$ information to break the sky-synchronous degeneracies inherent in the $\mathit{WMAP}$ scanning strategy., Comment: 11 figures, submitted to A&A. Includes updated instrument model and changes addressing referee comments
Published: 2022
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40. BeyondPlanck XIII. Intensity foreground sampling, degeneracies, and priors

Author: Andersen, K. J., Herman, D., Aurlien, R., Banerji, R., Basyrov, A., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Eskilt, J. R., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Lunde, J. G. S., Maggio, G., Maino, D., Maris, M., Mennella, A., Paradiso, S., Partridge, B., Reinecke, M., San, M., Stutzer, N. -O., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present the intensity foreground algorithms and model employed within the BeyondPlanck analysis framework. The BeyondPlanck analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the $Planck$ Low Frequency Instrument (LFI) data analysis. Given the scope of the BeyondPlanck analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous $\texttt{Commander}$ component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: 1) joint foreground spectral parameter and amplitude sampling, building on ideas from Miramare; 2) component-based monopole determination; 3) joint spectral parameter and monopole sampling; and 4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BeyondPlanck data set is the peak frequency of the anomalous microwave emission component, for which we find $\nu_{\mathrm{p}}=25.3\pm0.5$ GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects., Comment: 27 pages, 23 figures, 3 tables, part 13 of the BeyondPlanck release. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science. Paper version as accepted by A&A
Published: 2022
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41. BeyondPlanck XVI. Limits on Large-Scale Polarized Anomalous Microwave Emission from Planck LFI and WMAP

Author: Herman, D., Hensley, B., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We constrain the level of polarized anomalous microwave emission (AME) on large angular scales using $\textit{Planck}$ LFI and $\textit{WMAP}$ polarization data within a Bayesian CMB analysis framework. We model synchrotron emission with a power-law spectral energy distribution, and the sum of AME and thermal dust emission through linear regression with the $\textit{Planck}$ HFI 353 GHz data. This template-based dust emission model allows us to constrain the level of polarized AME while making minimal assumptions on its frequency dependence. We neglect cosmic microwave background fluctuations, but show through simulations that these have a minor impact on the results. We find that the resulting AME polarization fraction confidence limit is sensitive to the polarized synchrotron spectral index prior, and for priors steeper than $\beta_{\mathrm{s}} = -3.1\pm0.1$ we find an upper limit of $p_{\mathrm{AME}}^{\rm max}\lesssim 0.6\,\%$ ($95\,\%$ confidence). In contrast, for $\beta_{\mathrm{s}}=-3.0\pm0.1$, we find a nominal detection of $p_{\mathrm{AME}}=2.5\pm1.0\,\%$ ($95\,\%$ confidence). These data are thus not strong enough to simultaneously and robustly constrain both polarized synchrotron emission and AME, and our main result is therefore a constraint on the AME polarization fraction explicitly as a function of $\beta_\mathrm{s}$. Combining the current $\textit{Planck}$ and $\textit{WMAP}$ observations with measurements from high-sensitivity low-frequency experiments such as C-BASS and QUIJOTE will be critical to improve these limits further., Comment: 10 pages, 9 figures, part 15 of the BeyondPlanck release. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science. Refereed, edited, and accepted by A&A. Added references, added arxiv IDs to BeyondPlanck papers. Figure update. Expanded discussion on AME models. Moved title from BeyondPlanck XV to BeyondPlanck XVI due to internal reordering
Published: 2022
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42. BeyondPlanck III. Commander3

Author: Galloway, M., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We describe the computational infrastructure for end-to-end Bayesian CMB analysis implemented by the BeyondPlanck collaboration. This code is called commander3, and provides a statistically consistent framework for global analysis of CMB and microwave observations, and may be useful for a wide range of legacy, current, and future experiments. The paper has three main goals. Firstly, we provide a high-level overview of the existing code base, aiming to guide readers who wish to extend and adapt the code according to their own needs, or to reimplement it from scratch in a different programming language. Secondly, we discuss some critical computational challenges that arise within any global CMB analysis framework, for instance in-memory compression of time-ordered data, FFT optimization, and parallelization and load-balancing. Thirdly, we quantify the CPU and RAM requirements for the current BeyondPlanck analysis, and find that a total of 1.5 TB of RAM is required for efficient analysis, and the total cost of a full Gibbs sample is 170 CPU-hrs, including both low-level processing and high-level component separation, which is well within the capabilities of current low-cost computing facilities. The existing code base is made publicly available under a GNU General Public Library (GPL) license., Comment: 16 Pages, 7 Figures. Part of the BeyondPlanck paper suite
Published: 2022
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43. BeyondPlanck VIII. Efficient Sidelobe Convolution and Correction through Spin Harmonics

Author: Galloway, M., Reinecke, M., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Suur-Uski, A. -S., Svalheim, T. L., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., and Zacchei, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We introduce a new formulation of the Conviqt convolution algorithm in terms of spin harmonics, and apply this to the problem of sidelobe correction for BeyondPlanck, the first end-to-end Bayesian Gibbs sampling framework for CMB analysis. We compare our implementation to the previous Planck LevelS implementation, and find good agreement between the two codes in terms of accuracy, but with a speed-up reaching a factor of 3--10, depending on the frequency bandlimits, $l_{\textrm{max}}$ and $m_{\textrm{max}}$. The new algorithm is significantly simpler to implement and maintain, since all low-level calculations are handled through an external spherical harmonic transform library. We find that our mean sidelobe estimates for Planck LFI agree well with previous efforts. Additionally, we present novel sidelobe rms maps that quantify the uncertainty in the sidelobe corrections due to variations in the sky model., Comment: 9 pages, 8 figures. Part of the BeyondPlanck paper suite
Published: 2022
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44. BeyondPlanck X. Bandpass and beam leakage corrections

Author: Svalheim, T. L., Andersen, K. J., Aurlien, R., Banerji, R., Bersanelli, M., Bertocco, S., Brilenkov, M., Carbone, M., Colombo, L. P. L., Eriksen, H. K., Foss, M. K., Franceschet, C., Fuskeland, U., Galeotta, S., Galloway, M., Gerakakis, S., Gjerløw, E., Hensley, B., Herman, D., Iacobellis, M., Ieronymaki, M., Ihle, H. T., Jewell, J. B., Karakci, A., Keihänen, E., Keskitalo, R., Maggio, G., Maino, D., Maris, M., Paradiso, S., Partridge, B., Reinecke, M., Suur-Uski, A. -S., Tavagnacco, D., Thommesen, H., Watts, D. J., Wehus, I. K., Zacchei, A., and Zonca, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We discuss the treatment of bandpass and beam leakage corrections in the Bayesian BeyondPlanck CMB analysis pipeline as applied to the Planck LFI measurements. As a preparatory step, we first apply three corrections to the nominal LFI bandpass profiles including removal of a known systematic effect in the ground measuring equipment at 61 GHz; smoothing of standing wave ripples; and edge regularization. The main net impact of these modifications is an overall shift in the 70 GHz bandpass of +0.6 GHz; we argue that any analysis of LFI data products, either from Planck or BeyondPlanck, should use these new bandpasses. In addition, we fit a single free bandpass parameter for each radiometer of the form $\Delta_i = \Delta_0 + \delta_i$, where $\Delta_0$ represents an absolute frequency shift per frequency band and $\delta_i$ is a relative shift per detector. The absolute correction is only fitted at 30 GHz with a full $\chi^2$-based likelihood, resulting in a correction of $\Delta_{30}=0.24\pm0.03\,$GHz. The relative corrections are fitted using a spurious map approach, fundamentally similar to the method pioneered by the WMAP team, but without introducing many additional degrees of freedom. All bandpass parameters are sampled using a standard Metropolis sampler within the main BeyondPlanck Gibbs chain, and bandpass uncertainties are thus propagated to all other data products in the analysis. In total, we find that our bandpass model significantly reduces leakage effects. For beam leakage corrections, we adopt the official Planck LFI beam estimates without additional degrees of freedom, and only marginalize over the underlying sky model. We note that this is the first time leakage from beam mismatch has been included for Planck LFI maps., Comment: 14 pages, 11 figures, this is part 10 in the BeyondPlanck release. All BeyondPlanck products and software will be released publicly at http://beyondplanck.science. Submitted to A&A
Published: 2022
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45. Euclid: Covariance of weak lensing pseudo-$C_\ell$ estimates. Calculation, comparison to simulations, and dependence on survey geometry

Author: Upham, R. E., Brown, M. L., Whittaker, L., Amara, A., Auricchio, N., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Marggraf, O., Markovic, K., Marulli, F., Meneghetti, M., 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., Raison, F., Rhodes, J., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Wang, Y., Zamorani, G., Zoubian, J., Andreon, S., Baldi, M., Camera, S., Cardone, V. F., Fabbian, G., Polenta, G., Renzi, A., Joachimi, B., Hall, A., Loureiro, A., and Sellentin, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Applications
Abstract: An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-$C_\ell$ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N-body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but further away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters describing matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10--20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component., Comment: 15 pages, 8 figures; matches version accepted by A&A; code available at https://github.com/robinupham/shear_pcl_cov
Published: 2021
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46. Euclid: Constraining ensemble photometric redshift distributions with stacked spectroscopy

Author: Cagliari, M. S., Granett, B. R., Guzzo, L., Bolzonella, M., Pozzetti, L., Tutusaus, I., Camera, S., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., 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., Cropper, M., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Ealet, A., Ferriol, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Garilli, B., Giocoli, C., 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., Kummel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H. -W., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Baldi, M., Farinelli, R., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., Vassallo, T., and Humphrey, A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The ESA Euclid mission will produce photometric galaxy samples over 15000 square degrees of the sky that will be rich for clustering and weak lensing statistics. The accuracy of the cosmological constraints derived from these measurements will depend on the knowledge of the underlying redshift distributions based on photometric redshift calibrations. A new approach is proposed to use the stacked spectra from Euclid slitless spectroscopy to augment broad-band photometric information to constrain the redshift distribution with spectral energy distribution fitting. The high spectral resolution available in the stacked spectra complements the photometry and helps to break the colour-redshift degeneracy and constrain the redshift distribution of galaxy samples. We modelled the stacked spectra as a linear mixture of spectral templates. The mixture may be inverted to infer the underlying redshift distribution using constrained regression algorithms. We demonstrate the method on simulated Vera C. Rubin Observatory and Euclid mock survey data sets based on the Euclid Flagship mock galaxy catalogue. We assess the accuracy of the reconstruction by considering the inference of the baryon acoustic scale from angular two-point correlation function measurements. We selected mock photometric galaxy samples at redshift z>1 using the self-organising map algorithm. Considering the idealised case without dust attenuation, we find that the redshift distributions of these samples can be recovered with 0.5% accuracy on the baryon acoustic scale. The estimates are not significantly degraded by the spectroscopic measurement noise due to the large sample size. However, the error degrades to 2% when the dust attenuation model is left free. We find that the colour degeneracies introduced by attenuation limit the accuracy considering the wavelength coverage of Euclid near-infrared spectroscopy., Comment: 16 pages, 10 figures. Accepted by A&A (Jan. 18)
Published: 2021
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47. Euclid: Forecasts from redshift-space distortions and the Alcock-Paczynski test with cosmic voids

Author: Hamaus, N., Aubert, M., Pisani, A., Contarini, S., Verza, G., Cousinou, M. -C., Escoffier, S., Hawken, A., Lavaux, G., Pollina, G., Wandelt, B. D., Weller, J., Bonici, M., Carbone, C., Guzzo, L., Kovacs, A., Marulli, F., Massara, E., Moscardini, L., Ntelis, P., Percival, W. J., Radinović, S., Sahlén, M., Sakr, Z., Sánchez, A. G., Winther, H. A., Auricchio, N., Awan, S., 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., Conversi, L., Copin, Y., Corcione, L., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Massey, R., Maurogordato, S., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Rebolo, R., Rhodes, J., Rix, H., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Starck, J. -L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Welikala, N., Zamorani, G., Zoubian, J., Andreon, S., Baldi, M., Camera, S., Mei, S., Neissner, C., and Romelli, E.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Euclid is poised to survey galaxies across a cosmological volume of unprecedented size, providing observations of more than a billion objects distributed over a third of the full sky. Approximately 20 million of these galaxies will have their spectroscopy available, allowing us to map the 3D large-scale structure of the Universe in great detail. This paper investigates prospects for the detection of cosmic voids therein and the unique benefit they provide for cosmology. In particular, we study the imprints of dynamic and geometric distortions of average void shapes and their constraining power on the growth of structure and cosmological distance ratios. To this end, we made use of the Flagship mock catalog, a state-of-the-art simulation of the data expected to be observed with Euclid. We arranged the data into four adjacent redshift bins, each of which contains about 11000 voids and estimated the stacked void-galaxy cross-correlation function in every bin. Fitting a linear-theory model to the data, we obtained constraints on $f/b$ and $D_M H$, where $f$ is the linear growth rate of density fluctuations, $b$ the galaxy bias, $D_M$ the comoving angular diameter distance, and $H$ the Hubble rate. In addition, we marginalized over two nuisance parameters included in our model to account for unknown systematic effects. With this approach, Euclid will be able to reach a relative precision of about 4% on measurements of $f/b$ and 0.5% on $D_M H$ in each redshift bin. Better modeling or calibration of the nuisance parameters may further increase this precision to 1% and 0.4%, respectively. Our results show that the exploitation of cosmic voids in Euclid will provide competitive constraints on cosmology even as a stand-alone probe. For example, the equation-of-state parameter $w$ for dark energy will be measured with a precision of about 10%, consistent with previous more approximate forecasts., Comment: 15 pages, 7 figures. Accepted by A&A (Oct. 31)
Published: 2021
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48. 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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49. Euclid: constraining dark energy coupled to electromagnetism using astrophysical and laboratory data

Author: Martinelli, M., Martins, C. J. A. P., Nesseris, S., Tutusaus, I., Blanchard, A., Camera, S., Carbone, C., Casas, S., Pettorino, V., Sakr, Z., Yankelevich, V., Sapone, D., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Corcione, L., Costille, A., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Ealet, A., Ferriol, S., Frailis, M., Franceschi, E., Garilli, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kiessling, A., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Poncet, M., Popa, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Starck, J. -L., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valenziano, L., Wang, Y., Zamorani, G., Zoubian, J., Baldi, M., Brescia, M., Congedo, G., Conversi, L., Copin, Y., Fabbian, G., Farinelli, R., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., and Vassallo, T.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: In physically realistic scalar-field based dynamical dark energy models (including, e.g., quintessence) one naturally expects the scalar field to couple to the rest of the model's degrees of freedom. In particular, a coupling to the electromagnetic sector leads to a time (redshift) dependence of the fine-structure constant and a violation of the Weak Equivalence Principle. Here we extend the previous Euclid forecast constraints on dark energy models to this enlarged (but physically more realistic) parameter space, and forecast how well Euclid, together with high-resolution spectroscopic data and local experiments, can constrain these models. Our analysis combines simulated Euclid data products with astrophysical measurements of the fine-structure constant, $\alpha$, and local experimental constraints, and includes both parametric and non-parametric methods. For the astrophysical measurements of $\alpha$ we consider both the currently available data and a simulated dataset representative of Extremely Large Telescope measurements and expected to be available in the 2030s. Our parametric analysis shows that in the latter case the inclusion of astrophysical and local data improves the Euclid dark energy figure of merit by between $8\%$ and $26\%$, depending on the correct fiducial model, with the improvements being larger in the null case where the fiducial coupling to the electromagnetic sector is vanishing. These improvements would be smaller with the current astrophysical data. Moreover, we illustrate how a genetic algorithms based reconstruction provides a null test for the presence of the coupling. Our results highlight the importance of complementing surveys like Euclid with external data products, in order to accurately test the wider parameter spaces of physically motivated paradigms., Comment: 14 pages, 7 figures. Submitted to Astronomy & Astrophysics
Published: 2021
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50. Euclid: Estimation of the impact of correlated readout noise for flux measurements with the Euclid NISP instrument

Author: Munoz, A. Jimenez, Macias-Perez, J., Secroun, A., Gillard, W., Kubik, B., Auricchio, N., Balestra, A., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Casas, R., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Ealet, A., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kummel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Medinaceli, E., Mei, S., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pires, S., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Roncarelli, M., Rossetti, E., Saglia, R., Sauvage, M., Scaramella, R., Schneider, P., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Valenziano, L., Vassallo, T., Kleijn, G. A. Verdoes, Wang, Y., Weller, J., Wetzstein, M., Zamorani, G., and Zoubian, J.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Euclid satellite, to be launched by ESA in 2022, will be a major instrument for cosmology for the next decades. \Euclid\ is composed of two instruments: the Visible (VIS) instrument and the Near Infrared Spectromete and Photometer (NISP). In this work we estimate the implications of correlated readout noise in the NISP detectors for the final in-flight flux measurements. Considering the multiple accumulated (MACC) readout mode, for which the UTR (Up The Ramp) exposure frames are averaged in groups, we derive an analytical expression for the noise covariance matrix between groups in the presence of correlated noise. We also characterize the correlated readout noise properties in the NISP engineering grade detectors using long dark integrations. For this purpose, we assume a $(1/f)^{\, \alpha}$-like noise model and fit the model parameters to the data, obtaining typical values of $\sigma = 19.7^{+1.1}_{-0.8}$ e$^{-} \rm{Hz}^{-0.5}$, $f_{\rm{knee}} = (5.2^{+1.8}_{-1.3}) \times 10^{-3} \, \rm{Hz}$ and $\alpha = 1.24 ^{+0.26}_{-0.21}$. Furthermore, via realistic simulations and using a maximum likelihood flux estimator we derive the bias between the input flux and the recovered one. We find that using our analytical expression for the covariance matrix of the correlated readout noise we diminish this bias by up to a factor of four with respect to the white noise approximation for the covariance matrix. Finally, we conclude that the final bias on the in-flight NISP flux measurements should still be negligible even in the white noise approximation, which is taken as a baseline for the Euclid\on-board processing
Published: 2021
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