Author: "Huertas-Company, M" - Searchworks@Jio Institute Digital Library Search Results

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484 results on '"Huertas-Company, M"'

1. COSMOS-Web: stellar mass assembly in relation to dark matter halos across $0.2<z<12$ of cosmic history

Author: Shuntov, M., Ilbert, O., Toft, S., Arango-Toro, R. C., Akins, H. B., Casey, C. M., Franco, M., Harish, S., Kartaltepe, J. S., Koekemoer, A. M., McCracken, H. J., Paquereau, L., Laigle, C., Bethermin, M., Dubois, Y., Drakos, N. E., Faisst, A., Gozaliasl, G., Gillman, S., Hayward, C. C., Hirschmann, M., Huertas-Company, M., Jespersen, C. K., Jin, S., Kokorev, V., Lambrides, E., Borgne, D. Le, Liu, D., Magdis, G., Massey, R., McPartland, C. J. R., Mercier, W., McCleary, J. E., McKinney, J., Oesch, P. A., Rhodes, J. D., Rich, R. M., Robertson, B. E., Sanders, D., Trebitsch, M., Tresse, L., Valentino, F., Vijayan, A. P., Weaver, J. R., Weibel, A., and Wilkins, S. M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We study the stellar mass function (SMF) and the co-evolution with dark matter halos via abundance matching in the largest redshift range to date $0.25$, we find increased abundances of massive (log$\, M_{\star}/M_{\odot}>10.5$) implying integrated star formation efficiencies (SFE) $\epsilon_{\star}\equiv M_{\star}\, f_{\rm b}^{-1} M_{\rm halo}^{-1} \gtrsim 0.5$. We find a flattening of the SMF at the high-mass end that is better described by a double power law at $z>5.5$. At $z \lesssim 5.5$ it transitions to a Schechter law which coincides with the emergence of the first massive quiescent galaxies in the Universe. We trace the cosmic stellar mass density (SMD) and infer the star formation rate density (SFRD), which at $z>7.5$ agrees remarkably with recent \JWST{} UV luminosity function-derived estimates. However, at $z \lesssim 3.5$, we find significant tension ($\sim 0.3$ dex) with the cosmic star formation (SF) history from instantaneous SF measures, the causes of which remain poorly understood. We infer the stellar-to-halo mass relation (SHMR) and the SFE from abundance matching out to $z=12$, finding a non-monotonic evolution. The SFE has the characteristic strong dependence with mass in the range of $0.02 - 0.2$, and mildly decreases at the low mass end out to $z\sim3.5$. At $z\sim3.5$ the SFE increases sharply from $\sim 0.1$ to approach high SFE of $0.8-1$ by $z\sim 10$ for log$(M_{\rm h}/M_{\odot})\approx11.5$, albeit with large uncertainties. Finally, we use the SHMR to track the SFE and stellar mass growth throughout the halo history and find that they do not grow at the same rate -- from the earliest times up until $z\sim3.5$ the halo growth rate outpaces galaxy assembly, but at $z>3.5$ halo growth stagnates and accumulated gas reservoirs keep the SF going and galaxies outpace halos.
Published: 2024

2. A history of galaxy migrations over the Stellar Mass - SFR plane from the COSMOS-Web survey

Author: Arango-Toro, R. C., Ilbert, O., Ciesla, L., Shuntov, M., Aufort, G., Mercier, W., Laigle, C., Franco, M., Bethermin, M., Borgne, D. Le, Dubois, Y., McCracken, H. J., Paquereau, L., Huertas-Company, M., Kartaltepe, J., Casey, C. M., Akins, H., Allen, N., Andika, I., Brinch, M., Drakos, N. E., Faisst, A., Gozaliasl, G., Harish, S., Kaminsky, A., Koekemoer, A., Kokorev, V., Liu, D., Magdis, G., Martin, C. L., Moutard, T., Rhodes, J., Rich, R. M., Robertson, B., Sanders, D. B., Sheth, K., Talia, M., Toft, S., Tresse, L., Valentino, F., Vijayan, A., and Weaver, J.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The stellar mass-star formation rate (M$_\star$ - SFR) plane is essential for distinguishing galaxy populations, but how galaxies move within this plane over cosmic time remains unclear. This study aims to describe galaxy migrations in the M$_\star$ - SFR plane by reconstructing star formation histories (SFHs) for a sample of galaxies out to redshift $z < 4$. This provides insights into the physical processes driving star formation. We use data from the COSMOS field, selecting 299131 galaxies at $z < 4$ with COSMOS-Web NIRCam data (m$_\mathrm{F444W} < 27$) over 0.54 deg$^2$. Using the SED modeling code CIGALE with non-parametric SFHs, we derive physical properties and migration vectors for these galaxies. These vectors describe the direction and velocity of evolutionary paths across the M$_\star$ - SFR plane. To assess the accuracy of these vectors, we compare them to results from the Horizon-AGN simulation. Galaxies within the main sequence show low migration amplitudes and dispersed movement directions, indicating oscillation within the main sequence. Most progenitors were already on the main sequence a billion years earlier. Starburst galaxies assembled half their mass in the last 350 Myr and originated from the main sequence. Passive galaxies show uniformly declining SFHs and include massive galaxies already in the passive region at $z = 3.5-4$, with increasing density over time. Using reconstructed SFHs up to $z < 4$, we propose a coherent picture of how galaxies migrate over cosmic time in the M$_\star$ - SFR plane, highlighting the connection between major phases in the star-formation history.
Published: 2024

3. Euclid Preparation. Cosmic Dawn Survey: Data release 1 multiwavelength catalogues for Euclid Deep Field North and Euclid Deep Field Fornax

Author: Euclid Collaboration, Zalesky, L., McPartland, C. J. R., Weaver, J. R., Toft, S., Sanders, D. B., Mobasher, B., Suzuki, N., Szapudi, I., Valdes, I., Murphree, G., Chartab, N., Allen, N., Taamoli, S., Barrow, S. W. J., Ortiz, O. Chávez, Finkelstein, S. L., Gwyn, S., Sawicki, M., McCracken, H. J., Stern, D., Dannerbauer, H., Altieri, B., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., 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., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Neissner, 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., Sapone, D., Scaramella, R., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., 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., Zamorani, G., Zucca, E., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Mauri, N., Nucita, A. A., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Chary, R., Contarini, S., Contini, T., Cooray, A. R., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferrero, I., Finelli, F., 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., Muñoz, 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., 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., Odier, J., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Scarlata, C., Schneider, A., Sereno, M., Silvestri, A., 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 Zinchenko, I. A.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Cosmic Dawn Survey (DAWN survey) provides multiwavelength (UV/optical to mid-IR) data across the combined 59 deg$^{2}$ of the Euclid Deep and Auxiliary fields (EDFs and EAFs). Here, the first public data release (DR1) from the DAWN survey is presented. DR1 catalogues are made available for a subset of the full DAWN survey that consists of two Euclid Deep fields: Euclid Deep Field North (EDF-N) and Euclid Deep Field Fornax (EDF-F). The DAWN survey DR1 catalogues do not include $Euclid$ data as they are not yet public for these fields. Nonetheless, each field has been covered by the ongoing Hawaii Twenty Square Degree Survey (H20), which includes imaging from CFHT MegaCam in the new $u$ filter and from Subaru Hyper Suprime-Cam (HSC) in the $griz$ filters. Each field is further covered by $Spitzer$/IRAC 3.6-4.5$\mu$m imaging spanning 10 deg$^{2}$ and reaching $\sim$25 mag AB (5$\sigma$). All present H20 imaging and all publicly available imaging from the aforementioned facilities are combined with the deep $Spitzer$/IRAC data to create source catalogues spanning a total area of 16.87 deg$^{2}$ in EDF-N and 2.85 deg$^{2}$ in EDF-F for this first release. Photometry is measured using The Farmer, a well-validated model-based photometry code. Photometric redshifts and stellar masses are computed using two independent codes for modeling spectral energy distributions: EAZY and LePhare. Photometric redshifts show good agreement with spectroscopic redshifts ($\sigma_{\rm NMAD} \sim 0.5, \eta < 8\%$ at $i < 25$). Number counts, photometric redshifts, and stellar masses are further validated in comparison to the COSMOS2020 catalogue. The DAWN survey DR1 catalogues are designed to be of immediate use in these two EDFs and will be continuously updated. Future data releases will provide catalogues of all EDFs and EAFs and include $Euclid$ data.
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. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission

Author: Euclid Collaboration, Castander, F. J., Fosalba, P., Stadel, J., Potter, D., Carretero, J., Tallada-Crespí, P., Pozzetti, L., Bolzonella, M., Mamon, G. A., Blot, L., Hoffmann, K., Huertas-Company, M., Monaco, P., Gonzalez, E. J., De Lucia, G., Scarlata, C., Breton, M. -A., Linke, L., Viglione, C., Li, S. -S., Zhai, Z., Baghkhani, Z., Pardede, K., Neissner, C., Teyssier, R., Crocce, M., Tutusaus, I., Miller, L., Congedo, G., Biviano, A., Hirschmann, M., Pezzotta, A., Aussel, H., Hoekstra, H., Kitching, T., Percival, W. J., Guzzo, L., Mellier, Y., Oesch, P. A., Bowler, R. A. A., Bruton, S., Allevato, V., Gonzalez-Perez, V., Manera, M., Avila, S., Kovács, A., Aghanim, N., Altieri, B., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., 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., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Grupp, F., Haugan, S. V. H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Laureijs, R., Mignant, D. Le, 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., Masters, D. C., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paech, K., Paltani, S., Pasian, F., Peacock, J. A., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Schirmer, M., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tsyganov, A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zerbi, F. M., Zoubian, J., Zucca, E., Baccigalupi, C., Bernardeau, F., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Casenove, P., Castignani, G., Colodro-Conde, C., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Finelli, F., Gracia-Carpio, J., Ilić, S., Liebing, P., Marcin, S., Martinelli, M., Matthew, S., Mauri, N., Pöntinen, M., Porciani, C., Sakr, Z., Scottez, V., Sefusatti, E., Steinwagner, J., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Aubourg, E., Balaguera-Antolinez, A., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Böhringer, H., Borgani, S., Bouvard, T., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Canas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Costanzi, M., 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., 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., Gaztanaga, E., Giacomini, F., Gianotti, F., Gonzalez, A. H., Gozaliasl, G., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Holland, A. D., Ilbert, O., Joudaki, S., Jullo, E., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Migliaccio, M., Miluzio, M., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Nicastro, L., Walton, Nicholas A., Oguri, M., Patrizii, L., Popa, V., Pourtsidou, A., Reimberg, P., Risso, I., Rocci, P. -F., Rollins, R. P., Rusholme, B., Sahlén, M., Sánchez, A. G., Schaye, J., Schewtschenko, J. A., Schneider, A., Schultheis, M., Sereno, M., Shankar, F., Shulevski, A., Silvestri, A., Simon, P., Mancini, A. Spurio, Stanford, S. A., Tanidis, K., Tao, C., Tessore, N., Testera, G., Tewes, M., 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., Dimauro, P., Duc, P. -A., Fang, Y., Ferguson, A. M. N., Gutierrez, C. M., Kova{č}ić, I., Kruk, S., Brun, A. M. C. Le, Montoro, A., Murray, C., Pagano, L., Paoletti, D., Sarpa, E., Viitanen, A., Martín-Fleitas, J., and Yung, L. Y. A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breath of Euclid's data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific analyses. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z=3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of H_E<26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission's main cosmological analysis. (abridged), 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

6. 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

7. 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

8. 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., Alvi, S., 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., Baron, M., Barreiro, T., Barrena, R., 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., Bianchi, D., Bisigello, L., Biviano, A., Blake, R. P., Blanchard, A., Blazek, J., Blot, L., Bosco, A., Bodendorf, C., Boenke, T., Böhringer, H., Boldrini, P., 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., Bouwens, R., Bouy, H., Bowler, R. A. A., Bozza, V., Bozzo, E., Branchini, E., Brando, G., 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., Doumerg, W. d'Assignies, Daste, G., Davies, J. E., Davini, S., Dayal, P., 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., Frenk, C. . S., 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., Gerbino, M., 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-Perez, V., 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., Jain, B., 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., Kovačić, 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., Laurent, V., 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., Reun, A. Le, Leroy, G., 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., Maggio, G., 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., Mottet, S., 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., Nouri-Zonoz, 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., Pierre, M., 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., Rasera, Y., 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., Sartoris, B., 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., Schuster, N., 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., Setnikar, G., 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., Valageas, P., Valdes, I., Valentijn, E. A., Valenziano, L., Valieri, C., Valiviita, J., Broeck, M. Van den, Vassallo, T., Vavrek, R., Vega-Ferrero, J., 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., Wetzstein, M., Whalen, D. J., Whittam, I. H., Widmer, A., Wiesmann, M., Wilde, J., Williams, O. R., Winther, H. -A., Wittje, A., Wong, J. H. W., Wright, A. H., Yankelevich, V., Yeung, H. W., Yoon, M., 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: Accepted for publication in the A&A special issue`Euclid on Sky'
Published: 2024

9. 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

10. Galaxy merger challenge: A comparison study between machine learning-based detection methods

Author: Margalef-Bentabol, B., Wang, L., La Marca, A., Blanco-Prieto, C., Chudy, D., Domínguez-Sánchez, H., Goulding, A. D., Guzmán-Ortega, A., Huertas-Company, M., Martin, G., Pearson, W. J., Rodriguez-Gomez, V., Walmsley, M., Bickley, R. W., Bottrell, C., Conselice, C., and O'Ryan, D.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1
Published: 2024
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11. Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

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

Author: Euclid Collaboration, Serrano, S., Hudelot, P., Seidel, G., Pollack, J. E., Jullo, E., Torradeflot, F., Benielli, D., Fahed, R., Auphan, T., Carretero, J., Aussel, H., Casenove, P., Castander, F. J., Davies, J. E., Fourmanoit, N., Huot, S., Kara, A., Keihänen, E., Kermiche, S., Okumura, K., Zoubian, J., Ealet, A., Boucaud, A., Bretonnière, H., Casas, R., Clément, B., Duncan, C. A. J., George, K., Kiiveri, K., Kurki-Suonio, H., Kümmel, M., Laugier, D., Mainetti, G., Mohr, J. J., Montoro, A., Neissner, C., Rosset, C., Schirmer, M., Tallada-Crespí, P., Tonello, N., Venhola, A., Verderi, A., Zacchei, A., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Azzollini, R., Baccigalupi, C., Baldi, M., Bardelli, S., Basset, A., Battaglia, P., Bernardeau, F., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Crocce, M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Granett, B. R., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Joachimi, B., Kiessling, A., Kitching, T., Kohley, R., Kunz, M., Boulc'h, Q. Le, Liebing, P., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marcin, S., 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., Morris, P., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. -M., Nutma, T., 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., Rusholme, B., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Sauvage, M., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Starck, J. -L., Steinwagner, J., Taylor, A. N, Teplitz, H., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Bozzo, E., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mauri, N., Scottez, V., Tenti, M., Akrami, Y., Allevato, V., Ballardini, M., Blanchard, A., Borgani, S., Borlaff, A. S., Bruton, S., Burigana, C., Cappi, A., Carvalho, C. S., Castro, T., Cañas-Herrera, G., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., Desai, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Gabarra, L., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Gregorio, A., Hildebrandt, H., Huertas-Company, M., Ilbert, O., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Schneider, A., Sereno, M., Shulevski, A., Simon, P., Mancini, A. Spurio, Stadel, J., Tewes, M., Teyssier, R., Toft, S., Tucci, M., Valiviita, J., Viel, M., and Zinchenko, I. A.
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The European Space Agency's Euclid mission is one of the upcoming generation of large-scale cosmology surveys, which will map the large-scale structure in the Universe with unprecedented precision. The development and validation of the SGS pipeline requires state-of-the-art simulations with a high level of complexity and accuracy that include subtle instrumental features not accounted for previously as well as faster algorithms for the large-scale production of the expected Euclid data products. In this paper, we present the Euclid SGS simulation framework as applied in a large-scale end-to-end simulation exercise named Science Challenge 8. Our simulation pipeline enables the swift production of detailed image simulations for the construction and validation of the Euclid mission during its qualification phase and will serve as a reference throughout operations. Our end-to-end simulation framework starts with the production of a large cosmological N-body & mock galaxy catalogue simulation. We perform a selection of galaxies down to I_E=26 and 28 mag, respectively, for a Euclid Wide Survey spanning 165 deg^2 and a 1 deg^2 Euclid Deep Survey. We build realistic stellar density catalogues containing Milky Way-like stars down to H<26. Using the latest instrumental models for both the Euclid instruments and spacecraft as well as Euclid-like observing sequences, we emulate with high fidelity Euclid satellite imaging throughout the mission's lifetime. We present the SC8 data set consisting of overlapping visible and near-infrared Euclid Wide Survey and Euclid Deep Survey imaging and low-resolution spectroscopy along with ground-based. This extensive data set enables end-to-end testing of the entire ground segment data reduction and science analysis pipeline as well as the Euclid mission infrastructure, paving the way to future scientific and technical developments and enhancements., Comment: 39 pages, 25 figures, A&A submitted
Published: 2024
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13. Euclid Preparation. XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

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

14. The COSMOS-Web ring: in-depth characterization of an Einstein ring lensing system at z~2

Author: Mercier, W., Shuntov, M., Gavazzi, R., Nightingale, J. W., Arango, R., Ilbert, O., Amvrosiadis, A., Ciesla, L., Casey, C., Jin, S., Faisst, A. L., Andika, I. T., Drakos, N. E., Enia, A., Franco, M., Gillman, S., Gozaliasl, G., Hayward, C. C., Huertas-Company, M., Kartaltepe, J. S., Koekemoer, A. M., Laigle, C., Borgne, D. Le, Magdis, G., Mahler, G., Maraston, C., Martin, C. L., Massey, R., McCracken, H. J., Moutard, T., Paquereau, L., Rhodes, J. D., Robertson, B. E., Sanders, D. B., Trebitsch, M., Tresse, L., and Vijayan, A. P.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Aims. We provide an in-depth analysis of the COSMOS-Web ring, an Einstein ring at z=2 that we serendipitously discovered in the COSMOS-Web survey and possibly the most distant lens discovered to date. Methods. We extract the visible and NIR photometry from more than 25 bands and we derive the photometric redshifts and physical properties of both the lens and the source with three different SED fitting codes. Using JWST/NIRCam images, we also produce two lens models to (i) recover the total mass of the lens, (ii) derive the magnification of the system, (iii) reconstruct the morphology of the lensed source, and (iv) measure the slope of the total mass density profile of the lens. Results. The lens is a very massive and quiescent (sSFR < 10^(-13) yr-1) elliptical galaxy at z = 2.02 \pm 0.02 with a total mass Mtot(
Published: 2023
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15. Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Author: Euclid Collaboration, Leuzzi, L., Meneghetti, M., Angora, G., Metcalf, R. B., Moscardini, L., Rosati, P., Bergamini, P., Calura, F., Clément, B., Gavazzi, R., Gentile, F., Lochner, M., Grillo, C., Vernardos, G., Aghanim, N., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Joachimi, B., Kümmel, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kunz, M., Kurki-Suonio, H., 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., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Nightingale, J. W., Nutma, T., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Boucaud, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Farinelli, R., Graciá-Carpio, J., Mauri, N., Neissner, C., Scottez, V., Tenti, M., Tramacere, A., Akrami, Y., Allevato, V., Baccigalupi, C., Ballardini, M., Bernardeau, F., Biviano, A., Borgani, S., Borlaff, A. S., Bretonnière, H., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., De Lucia, G., Desprez, G., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Gabarra, L., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., George, K., Gozaliasl, G., Hildebrandt, H., Huertas-Company, M., 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., Maurin, L., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Pöntinen, M., Patrizii, L., Popa, V., Porciani, C., Potter, D., Reimberg, P., Sánchez, A. G., Sakr, Z., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Steinwagner, J., Teyssier, R., Valiviita, J., Viel, M., Zinchenko, I. A., and Sánchez, H. Domínguez
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models., Comment: 23 pages,12 figures
Published: 2023
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16. Ask The Machine: Systematic detection of wind-type outflows in low-mass X-ray binaries

Author: Sánchez, D. Mata, Muñoz-Darias, T., Casares, J., Huertas-Company, M., and Panizo-Espinar, G.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: The systematic discovery of outflows in the optical spectra of low-mass X-ray binaries opened a new avenue for the study of the outburst evolution in these extreme systems. However, the efficient detection of such features in a continuously growing database requires the development of new analysis techniques with a particular focus on scalability, adaptability, and automatization. In this pilot study, we explore the use of machine learning algorithms to perform the identification of outflows in spectral line profiles observed in the optical range. We train and test the classifier on a simulated database, constructed through a combination of disc emission line profiles and outflow signatures, emulating typical observations of low-mass X-ray binaries. The final, trained classifier is applied to two sets of spectra taken during two bright outbursts that were particularly well covered, those of V404 Cyg (2015) and MAXI J1820+070 (2018). The resulting classification gained by this novel approach is overall consistent with that obtained through traditional techniques, while it simultaneously provides a number of key advantages over the latter, including the access to low velocity outflows. This study sets the foundations for future studies on large samples of spectra from low-mass X-ray binaries and other compact binaries., Comment: Accepted for publication in MNRAS. 13 pages, 6 figures
Published: 2023
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17. Galaxy Morphology from $z\sim6$ through the eyes of JWST

Author: Huertas-Company, M., Iyer, K. G., Angeloudi, E., Bagley, M. B., Finkelstein, S. L., Kartaltepe, J., Sarmiento, R., Vega-Ferrero, J., Haro, P. Arrabal, Behroozi, P., Buitrago, F., Cheng, Y., Costantin, L., Dekel, A., Dickinson, M., Elbaz, D., Grogin, N. A., Hathi, N. P., Holwerda, B. W., Koekemoer, A. M., Lucas, R. A., Papovich, C., Pérez-González, P. G., Pirzkal, N., Seillé, L-M., de la Vega, A., Wuyts, S., Yang, G., and Yung, L. Y. A.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We analyze the Near Infrared ($\sim0.8-1\mu$m) rest-frame morphologies of galaxies with $\log M_*/M_\odot>9$ in the redshift range $010.5$) at $z\sim5$, and bulge-dominated galaxies also exist at these early epochs, confirming a rich and evolved morphological diversity of galaxies $\sim1$ Gyr after the Big Bang. Finally, we find that the morphology-quenching relation is already in place for massive galaxies at $z>3$, with massive quiescent galaxies ($\log M_*/M_\odot>10.5$) being predominantly bulge-dominated., Comment: Submitted to A&A, comments welcome
Published: 2023

18. CEERS Key Paper VI: JWST/MIRI Uncovers a Large Population of Obscured AGN at High Redshifts

Author: Yang, G., Caputi, K. I., Papovich, C., Haro, P. Arrabal, Bagley, M. B., Behroozi, P., Bell, E. F., Bisigello, L., Buat, V., Burgarella, D., Cheng, Y., Cleri, N. J., Dave, R., Dickinson, M., Elbaz, D., Ferguson, H. C., Finkelstein, S. L., Grogin, N. A., Hathi, N. P., Hirschmann, M., Holwerda, B. W., Huertas-Company, M., Hutchison, T., Iani, E., Kartaltepe, J. S., Kirkpatrick, A., Kocevski, D. D., Koekemoer, A. M., Kokorev, V., Larson, R. L., Lucas, R. A., Perez-Gonzalez, P. G., Rinaldi, P., Shen, L., Trump, J. R., de la Vega, A., Yung, L. Y. A., and Zavala, J. A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Mid-infrared observations are powerful in identifying heavily obscured Active Galactic Nuclei (AGN) which have weak emission in other wavelengths. Data from the Mid-Infrared Instrument (MIRI) onboard JWST provides an excellent opportunity to perform such studies. We take advantage of the MIRI imaging data from the Cosmic Evolution Early Release Science Survey (CEERS) to investigate the AGN population in the distant universe. We estimate the source properties of MIRI-selected objects by utilizing spectral energy distribution (SED) modelling, and classify them into star-forming galaxies (SF), SF-AGN mixed objects, and AGN. The source numbers of these types are 418, 111, and 31, respectively, from 4 MIRI pointings covering $\sim 9$ arcmin$^2$. The sample spans a redshift range of $\approx 0$--5. We derive the median SEDs for all three source types, respectively, and publicly release them. The median MIRI SED of AGN is similar to the typical SEDs of hot dust-obscured galaxies and Seyfert 2s, for which the mid-IR SEDs are dominantly from AGN-heated hot dust. Based on our SED-fit results, we estimate the black-hole accretion density (BHAD; i.e., total BH growth rate per comoving volume) as a function of redshift. At $z<3$, the resulting BHAD agrees with the X-ray measurements in general. At $z>3$, we identify a total of 27 AGN and SF-AGN mixed objects, leading to that our high-$z$ BHAD is substantially higher than the X-ray results ($\sim 0.5$ dex at $z \approx 3$--5). This difference indicates MIRI can identify a large population of heavily obscured AGN missed by X-ray surveys at high redshifts., Comment: ApJL in press, as part of the CEERS Focus Issue. 13 pages and 7 figures (median SEDs in Fig. 5 are available upon request)
Published: 2023
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19. Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks

Author: Sánchez, H. Domínguez, Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., Kado-Fong, E., Población-Criado, D., Souchereau, H., Leste, O. K., Robertson, B., Sahelices, B., and Johnston, K. V.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Interactions between galaxies leave distinguishable imprints in the form of tidal features which hold important clues about their mass assembly. Unfortunately, these structures are difficult to detect because they are low surface brightness features so deep observations are needed. Upcoming surveys promise several orders of magnitude increase in depth and sky coverage, for which automated methods for tidal feature detection will become mandatory. We test the ability of a convolutional neural network to reproduce human visual classifications for tidal detections. We use as training $\sim$6000 simulated images classified by professional astronomers. The mock Hyper Suprime Cam Subaru (HSC) images include variations with redshift, projection angle and surface brightness ($\mu_{lim}$ =26-35 mag arcsec$^{-2}$). We obtain satisfactory results with accuracy, precision and recall values of Acc=0.84, P=0.72 and R=0.85, respectively, for the test sample. While the accuracy and precision values are roughly constant for all surface brightness, the recall (completeness) is significantly affected by image depth. The recovery rate shows strong dependence on the type of tidal features: we recover all the images showing shell features and 87% of the tidal streams; these fractions are below 75% for mergers, tidal tails and bridges. When applied to real HSC images, the performance of the model worsens significantly. We speculate that this is due to the lack of realism of the simulations and take it as a warning on applying deep learning models to different data domains without prior testing on the actual data., Comment: 13 pages, 10 figures, accepted for publication in MNRAS
Published: 2023
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20. On the nature of disks at high redshift seen by JWST/CEERS with contrastive learning and cosmological simulations

Author: Vega-Ferrero, J., Huertas-Company, M., Costantin, L., Pérez-González, P. G., Sarmiento, R., Kartaltepe, J. S., Pillepich, A., Bagley, M. B., Finkelstein, S. L., McGrath, E. J., Knapen, J. H., Haro, P. Arrabal, Bell, E. F., Buitrago, F., Calabrò, A., Dekel, A., Dickinson, M., Sánchez, H. Domínguez, Elbaz, D., Ferguson, H. C., Giavalisco, M., Holwerda, B. W., Kocesvski, D. D., Koekemoer, A. M., Pandya, V., Papovich, C., Pirzkal, N., Primack, J., and Yung, L. Y. Aaron
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Visual inspections of the first optical rest-frame images from JWST have indicated a surprisingly high fraction of disk galaxies at high redshifts. Here, we alternatively apply self-supervised machine learning to explore the morphological diversity at $z \geq 3$. Our proposed data-driven representation scheme of galaxy morphologies, calibrated on mock images from the TNG50 simulation, is shown to be robust to noise and to correlate well with the physical properties of the simulated galaxies, including their 3D structure. We apply the method simultaneously to F200W and F356W galaxy images of a mass-complete sample ($M_*/M_\odot>10^9$) at $ 3 \leq z \leq 6$ from the first JWST/NIRCam CEERS data release. We find that the simulated and observed galaxies do not exactly populate the same manifold in the representation space from contrastive learning. We also find that half the galaxies classified as disks -- either CNN-based or visually -- populate a similar region of the representation space as TNG50 galaxies with low stellar specific angular momentum and non-oblate structure. Although our data-driven study does not allow us to firmly conclude on the true nature of these galaxies, it suggests that the disk fraction at $z \geq 3$ remains uncertain and possibly overestimated by traditional supervised classifications. Deeper imaging and spectroscopic follow-ups as well as comparisons with other simulations will help to unambiguously determine the true nature of these galaxies, and establish more robust constraints on the emergence of disks at very high redshift., Comment: 28 pages, 22 figures. Accepted for publication in ApJ
Published: 2023

21. 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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22. Euclid preparation XXVI. The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies

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

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

Author: Cheng, Ting-Yun, Sánchez, H. Domínguez, Vega-Ferrero, J., Conselice, C. J., Siudek, M., Aragón-Salamanca, A., Bernardi, M., Cooke, R., Ferreira, L., Huertas-Company, M., Krywult, J., Palmese, A., Pieres, A., Malagón, A. A. Plazas, Rosell, A. Carnero, Gruen, D., Thomas, D., Bacon, D., Brooks, D., James, D. J., Hollowood, D. L., Friedel, D., Suchyta, E., Sanchez, E., Menanteau, F., Paz-Chinchón, F., Gutierrez, G., Tarle, G., Sevilla-Noarbe, I., Ferrero, I., Annis, J., Frieman, J., García-Bellido, J., Mena-Fernández, J., Honscheid, K., Kuehn, K., da Costa, L. N., Gatti, M., Raveri, M., Pereira, M. E. S., Rodriguez-Monroy, M., Smith, M., Kind, M. Carrasco, Aguena, M., Swanson, M. E. C., Weaverdyck, N., Doel, P., Miquel, R., Ogando, R. L. C., Gruendl, R. A., Allam, S., Hinton, S. R., Dodelson, S., Bocquet, S., Desai, S., Everett, S., and Scarpine, V.
Subjects: Astrophysics - Astrophysics of Galaxies, Physics - Data Analysis, Statistics and Probability
Abstract: We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of $\sim$21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN - monochromatic images versus $gri$-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES ($i<18$), while the other is trained with bright galaxies ($r<17.5$) and `emulated' galaxies up to $r$-band magnitude $22.5$. Despite the different approaches, the agreement between the two catalogues is excellent up to $i<19$, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on $gri$-band images, at least in the bright regime. At fainter magnitudes, $i>19$, the overall agreement is good ($\sim$95\%), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases we are able to identify lenticular galaxies (at least up to $i<19$), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies., Comment: 17 pages, 14 figures (1 appendix for galaxy examples including 3 figures)
Published: 2022
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25. A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Author: Expósito-Márquez, J., Brook, C. B., Huertas-Company, M., Di Cintio, A., Macciò, A. V., Grand, R. J. J., and Battaglia, G.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM., Comment: 14 pages, 11 figures, submitted to MNRAS. comments welcome
Published: 2022
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26. 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

27. Euclid preparation. XXI. Intermediate-redshift contaminants in the search for $z>6$ galaxies within the Euclid Deep Survey

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

Author: Cheng, T-Y, Sánchez, H Domínguez, Vega-Ferrero, J, Conselice, CJ, Siudek, M, Aragón-Salamanca, A, Bernardi, M, Cooke, R, Ferreira, L, Huertas-Company, M, Krywult, J, Palmese, A, Pieres, A, Malagón, AA Plazas, Rosell, A Carnero, Gruen, D, Thomas, D, Bacon, D, Brooks, D, James, DJ, Hollowood, DL, Friedel, D, Suchyta, E, Sanchez, E, Menanteau, F, Paz-Chinchón, F, Gutierrez, G, Tarle, G, Sevilla-Noarbe, I, Ferrero, I, Annis, J, Frieman, J, García-Bellido, J, Mena-Fernández, J, Honscheid, K, Kuehn, K, da Costa, LN, Gatti, M, Raveri, M, Pereira, MES, Rodriguez-Monroy, M, Smith, M, Kind, M Carrasco, Aguena, M, Swanson, MEC, Weaverdyck, N, Doel, P, Miquel, R, Ogando, RLC, Gruendl, RA, Allam, S, Hinton, SR, Dodelson, S, Bocquet, S, Desai, S, Everett, S, and Scarpine, V
Subjects: Space Sciences, Particle and High Energy Physics, Astronomical Sciences, Physical Sciences, methods: data analysis, methods: statistical, galaxies: structure, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics, Space sciences
Abstract: We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN - monochromatic images versus gri-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES (i < 18), while the other is trained with bright galaxies (r < 17.5) and 'emulated' galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies.
Published: 2022

29. From naked spheroids to disky galaxies: how do massive disk galaxies shape their morphology?

Author: Costantin, L., Pérez-González, P. G., Méndez-Abreu, J., Huertas-Company, M., Pampliega, B. Alcalde, Balcells, M., Barro, G., Ceverino, D., Dimauro, P., Sánchez, H. Domínguez, Espino-Briones, N., and Koekemoer, A. M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We investigate the assembly history of massive disk galaxies and describe how they shape their morphology through cosmic time. Using SHARDS and HST data, we modeled the surface brightness distribution of 91 massive galaxies at redshift $0.14
Published: 2022
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30. The building up of observed stellar scaling relations of massive galaxies and the connection to black hole growth in the TNG50 simulation

Author: Varma, S., Huertas-Company, M., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Dekel, A., Faber, S. M., Iglesias-Navarro, P., Koo, D. C., and Primack, J.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: [abridged] We study how mock-observed stellar morphological and structural properties of massive galaxies are built up between $z=0.5$ and $z=3$ in the TNG50 cosmological simulation. We generate mock images with the properties of the CANDELS survey and derive Sersic parameters and optical rest-frame morphologies as usually done in the observations. Overall, the simulation reproduces the observed evolution of the abundances of different galaxy morphological types of star-forming and quiescent galaxies. The $\log{M_*}-\log R_e$ and $\log{M_*}-\log\Sigma_1$ relations of the simulated star-forming and quenched galaxies also match the observed slopes and zeropoints to within 1-$\sigma$. In the simulation, galaxies increase their observed central stellar mass density ($\Sigma_1$) and transform in morphology from irregular/clumpy systems to normal Hubble-type systems in the Star Formation Main Sequence at a characteristic stellar mass of $\sim 10^{10.5}~M_\odot$. This morphological transformation is connected to the activity of the central Super Massive Black Holes (SMBHs). At low stellar masses ($10^9$ < $M_*/M_\odot$ < $10^{10}$) SMBHs grow rapidly, while at higher mass SMBHs switch into the kinetic feedback mode and grow more slowly. During this low-accretion phase, SMBH feedback leads to the quenching of star-formation, along with a simultaneous growth in $\Sigma_1$. More compact massive galaxies grow their SMBHs faster than extended ones of the same mass and end up quenching earlier. In the TNG50 simulation, SMBHs predominantly grow via gas accretion before galaxies quench, and $\Sigma_1$ increases substantially after SMBH growth slows down. The simulation predicts therefore that quiescent galaxies have higher $\Sigma_1$ values than star-forming galaxies for the same SMBH mass, which disagrees with alternative models, and may potentially be in tension with some observations., Comment: accepted for publication in MNRAS
Published: 2021
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31. SDSS-IV DR17: Final Release of MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs

Author: Sánchez, H. Domínguez, Margalef, B., Bernardi, M., and Huertas-Company, M.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from S\`ersic and S\`ersic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, half light radii, bulge-disk fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides Deep Learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties: e.g., a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies which were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): namely, the low-end of the T-Types are better recovered in this new version. The catalogue also includes a separation between Early- or Late-type Galaxies (ETG, LTG), which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (-1 < T-Type < 2), where the T-Type values show a large scatter. In addition, $k-$fold based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined., Comment: Accepted for publication in MNRAS. Catalogues will be released with SDSS Data Release 17
Published: 2021
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32. Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

Author: Euclid Collaboration, Bretonnière, H., Huertas-Company, M., Boucaud, A., Lanusse, F., Jullo, E., Merlin, E., Tuccillo, D., Castellano, M., Brinchmann, J., Conselice, C. J., Dole, H., Cabanac, R., Courtois, H. M., Castander, F. J., Duc, P. A., Fosalba, P., Guinet, D., Kruk, S., Kuchner, U., Serrano, S., Soubrie, E., Tramacere, A., Wang, L., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Capobianco, V., Carbone, C., Carretero, J., Cavuoti, S., Cimatti, A., Cledassou, R., Corcione, L., Costille, A., Degaudenzi, H., Douspis, M., Dubath, F., Dusini, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Melchior, M., Meneghetti, M., Meylan, G., Moscardini, L., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Starck, J. -L., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Baldi, M., Bardelli, S., Brau-Nogue, S., Brescia, M., Camera, S., Congedo, G., Conversi, L., Copin, Y., Duncan, C . A. J., Dupac, X., Farinelli, R., Gillis, B., Kermiche, S., Kohley, R., Marulli, F., Medinaceli, E., Mei, S., Moresco, M., Morin, B., Munari, E., Polenta, G., Romelli, E., Tallada-Crespí, P., Tenti, M., Torradeflot, F., Vassallo, T., Welikala, N., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolínez, A., Biviano, A., Borgani, S., Bozzo, E., Burigana, C., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Da Silva, A., de la Torre, S., Fabricius, M., Farina, M., Farrens, S., Ferreira, P. G., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gillard, W., Gozaliasl, G., Hook, I. M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Maurogordato, S., McCracken, H. J., Metcalf, R. B., Morgante, G., Morisset, N., Nakajima, R., Nightingale, J., Nucita, A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Sánchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Stanco, L., Teyssier, R., Tutusaus, I., Valiviita, J., Viel, M., Whittaker, L., and Knapen, J. H
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of $0.4\,\rm{deg}^2$ as it will be seen by the Euclid visible imager VIS and show that galaxy structural parameters are recovered with similar accuracy as for pure analytic S\'ersic profiles. Based on these simulations, we estimate that the Euclid Wide Survey will be able to resolve the internal morphological structure of galaxies down to a surface brightness of $22.5\,\rm{mag}\,\rm{arcsec}^{-2}$, and $24.9\,\rm{mag}\,\rm{arcsec}^{-2}$ for the Euclid Deep Survey. This corresponds to approximately $250$ million galaxies at the end of the mission and a $50\,\%$ complete sample for stellar masses above $10^{10.6}\,\rm{M}_\odot$ (resp. $10^{9.6}\,\rm{M}_\odot$) at a redshift $z\sim0.5$ for the wide (resp. deep) survey. The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies., Comment: 22 pages, 21 figures (6 in appendinx). Accepted for publication to A&A. Change in the title for Euclid submission chronology coherence. Few changes for clarification
Published: 2021
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33. The evolution of compact massive quiescent and starforming galaxies derived from the $R_e-R_h$ and $M_{\rm star}-M_h$ relations

Author: Zanisi, L., Shankar, F., Fu, H., Rodriguez-Puebla, A., Avila-Reese, V., Faisst, A., Daddi, E., Boco, L., Lapi, A., Giavalisco, M., Saracco, P., Buitrago, F., Huertas-Company, M., Puglisi, A., and Dekel, A.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The mean size ( effective radius $R_e$) of Massive Galaxies (MGs, $M_{\rm star}>10^{11.2}M_\odot$) is observed to increase steadily with cosmic time. It is still unclear whether this trend originates from the size growth of individual galaxies (via, e.g., mergers and/or AGN feedback) or from the inclusion of larger galaxies entering the selection at later epochs (progenitor bias). We here build a data-driven, flexible theoretical framework to probe the structural evolution of MGs. We assign galaxies to dark matter haloes via stellar mass-halo mass (SMHM) relations with varying high-mass slopes and scatters $\sigma_{\rm SMHM}$ in stellar mass at fixed halo mass, and assign sizes to galaxies using an empirically-motivated, constant and linear relationship between $R_e$ and the host dark matter halo radius $R_h$. We find that: 1) the fast mean size growth of MGs is well reproduced independently of the shape of the input SMHM relation; 2) the numbers of compact MGs grow steadily until $z\gtrsim2$ and fall off at lower redshifts, suggesting a lesser role of progenitor bias at later epochs; 3) a time-independent scatter $\sigma_{\rm SMHM}$ is consistent with a scenario in which compact starforming MGs transition into quiescent MGs in a few $10^8$yr with a negligible structural evolution during the compact phase, while a scatter increasing at high redshift implies significant size growth during the starforming phase. A robust measurement of the size function of MGs at high redshift can set strong constraints on the scatter of the SMHM relation and, by extension, on models of galaxy evolution., Comment: Accepted for publication in MNRAS. Comments still welcome
Published: 2021
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34. A duality in the origin of bulges and spheroidal galaxies

Author: Costantin, L., Pérez-González, P. G., Méndez-Abreu, J., Huertas-Company, M., Dimauro, P., Alcalde-Pampliega, B., Buitrago, F., Ceverino, D., Daddi, E., Domínguez-Sánchez, H., Espino-Briones, N., Hernán-Caballero, A., Koekemoer, A. M., and Rodighiero, G.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Studying the resolved stellar populations of the different structural components which build massive galaxies directly unveils their assembly history. We aim at characterizing the stellar population properties of a representative sample of bulges and pure spheroids in massive galaxies ($M_{\star}>10^{10}$ M$_{\odot}$) in the GOODS-N field. We take advantage of the spectral and spatial information provided by SHARDS and HST data to perform the multi-image spectro-photometrical decoupling of the galaxy light. We derive the spectral energy distribution separately for bulges and disks in the redshift range $0.14
Published: 2021
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35. Pushing automated morphological classifications to their limits with the Dark Energy Survey

Author: Vega-Ferrero, J., Sánchez, H. Domínguez, Bernardi, M., Huertas-Company, M., Morgan, R., Margalef, B., Aguena, M., Allam, S., Annis, J., Avila, S., Bacon, D., Bertin, E., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Choi, A., Conselice, C., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Ferrero, I., Fosalba, P., Frieman, J., García-Bellido, J., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hinton, S. R., Hollowood, D. L., Honscheid, K., Hoyle, B., Jarvis, M., Kim, A. G., Kuehn, K., Kuropatkin, N., Lima, M., Maia, M. A. G., Menanteau, F., Miquel, R., Ogando, R. L. C., Palmese, A., Paz-Chinchón, F., Plazas, A. A., Romer, A. K., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Tarsitano, F., To, C., Tucker, D. L., Varga, T. N., and Wilkinson, R. D.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with S\'ersic index (\textit{n}), ellipticity ($\epsilon$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date., Comment: Accepted for publication in MNRAS (2021 February 22); 17 pages, 16 figures
Published: 2020
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36. The miniJPAS survey: a preview of the Universe in 56 colours

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

Author: Huertas-Company, M., Guo, Y., Ginzburg, O., Lee, C. T., Mandelker, N., Metter, M., Primack, J. R., Dekel, A., Ceverino, D., Faber, S. M., Koo, D. C., Koekemoer, A., Snyder, G., Giavalisco, M., and Zhang, H.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of $\sim1500$ star forming galaxies at $1
Published: 2020
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38. Structural and Stellar Population Properties vs. Bulge Types in Sloan Digital Sky Survey Central Galaxies

Author: Luo, Yifei, Faber, S. M., Rodriguez-Puebla, Aldo, Woo, Joanna, Guo, Yicheng, Koo, David C., Primack, Joel R., Chen, Zhu, Yesuf, Hassen M., Lin, Lin, Barro, Guillermo, Fang, Jerome J., Pandya, Viraj, Huertas-Company, M., and Mao, Shude
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey central galaxies using the new bulge indicator $\Delta\Sigma_1$, which measures relative central stellar-mass surface density within 1 kpc. We compare $\Delta\Sigma_1$ to the established bulge-type indicator $\Delta\langle\mu_e\rangle$ from Gadotti (2009) and show that classifying by $\Delta\Sigma_1$ agrees well with $\Delta\langle\mu_e\rangle$. $\Delta\Sigma_1$ requires no bulge-disk decomposition and can be measured on SDSS images out to $z = 0.07$. Bulge types using it are mapped onto twenty different structural and stellar-population properties for 12,000 SDSS central galaxies with masses 10.0 < log $M_*$/$M_{\odot}$ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log-log relations vs. $\Delta\Sigma_1$ and $\Delta\langle\mu_e\rangle$ with only moderate scatter, while stellar-population parameters show a highly non-linear "elbow" in which specific star-formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star-forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star-formation rates at fixed density. The non-linear relation between central density and star-formation rate has been seen before, but this mapping onto bulge class is new. The wide range of star-formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar-populations evolve at different rates as galaxies begin to quench., Comment: 23 pages, 17 figures, published in MNRAS
Published: 2019
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39. The Hubble Sequence at $z\sim0$ in the IllustrisTNG simulation with deep learning

Author: Huertas-Company, M., Rodriguez-Gomez, V., Nelson, D., Pillepich, A., Bernardi, M., Domínguez-Sánchez, H., Genel, S., Pakmor, R., Snyder, G. F., and Vogelsberger, M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $\sim12,000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model finds that $\sim95\%$ of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite. However, the Stellar Mass Functions decomposed into different morphologies still show significant discrepancies with observations both at the low and high mass end. We find that the high mass end of the SMF is dominated in TNG by massive disk galaxies while early-type galaxies dominate in the observations according to the CNN classifications. The present work highlights the importance of detailed comparisons between observations and simulations in comparable conditions., Comment: submitted to MNRAS, comments welcome
Published: 2019
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40. Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

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

41. The host galaxies of luminous type 2 AGN at $z \sim$0.3-0.4

Author: Urbano-Mayorgas, J. J., Martín, M. Villar, Buitrago, F., López, J. Piqueras, del Pino, B. Rodríguez, Koekemoer, A. M., Huertas-Company, M., Domínguez-Tenreiro, R., Carrera, F. J., and Tadhunter, C.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We study the morphological and structural properties of the host galaxies associated with 57 optically-selected luminous type 2 AGN at $z\sim$0.3-0.4: 16 high-luminosity Seyfert 2 (HLSy2, 8.0$\le$log($L_{\rm [OIII]}/L_{\odot})<$8.3) and 41 obscured quasars (QSO2, log($L_{\rm [OIII]}/L_{\odot})\ge$8.3). With this work, the total number of QSO2 at $z<1$ with parametrized galaxies increases from $\sim$35 to 76. Our analysis is based on HST WFPC2 and ACS images that we fit with {\sc GALFIT}. HLSy2 and QSO2 show a wide diversity of galaxy hosts. The main difference lies in the higher incidence of highly-disturbed systems among QSO2. This is consistent with a scenario in which galaxy interactions are the dominant mechanism triggering nuclear activity at the highest AGN power. There is a strong dependence of galaxy properties with AGN power (assuming $L_ {\rm [OIII]}$ is an adequate proxy). The relative contribution of the spheroidal component to the total galaxy light (B/T) increases with $L_ {\rm [OIII]}$. While systems dominated by the spheoridal component spread across the total range of $L_ {\rm [OIII]}$, most disk-dominated galaxies concentrate at log($L_{\rm [OIII]}/L_{\odot})<$8.6. This is expected if more powerful AGN are powered by more massive black holes which are hosted by more massive bulges or spheroids. The average galaxy sizes ($\langle r_{\rm e} \rangle$) are 5.0$\pm$1.5 kpc for HLSy2 and 3.9$\pm$0.6 kpc for HLSy2 and QSO2 respectively. These are significantly smaller than those found for QSO1 and narrow line radio galaxies at similar $z$. We put the results of our work in context of related studies of AGN with quasar-like luminosities., Comment: 23 pages, 6 figures. Accepted for publication in MNRAS
Published: 2018
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42. Transfer learning for galaxy morphology from one survey to another

Author: Sánchez, H. Domínguez, Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., Buckley-Geer, E., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., D'Andrea, C. B., da Costa, L. N., Davis, C., De Vicente, J., Doel, P., Evrard, A. E., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Melchior, P., Menanteau, F., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schindler, R., Schubnell, M., Smith, M., Smith, R. C., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., Walker, A. R., and Zuntz, J.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study., Comment: Accepted for publication in MNRAS
Published: 2018
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43. On the Transition of the Galaxy Quenching Mode at 0.5<z<1 in CANDELS

Author: Liu, F. S., Jia, Meng, Yesuf, Hassen M., Faber, S. M., Koo, David C., Guo, Yicheng, Bell, Eric F., Jiang, Dongfei, Wang, Weichen, Koekemoer, Anton M., Zheng, Xianzhong, Fang, Jerome J., Barro, Guillermo, Pérez-González, Pablo G., Dekel, Avishai, Kocevski, Dale, Hathi, Nimish P., Croton, Darren, Huertas-Company, M., Meng, Xianmin, Tong, Wei, and Liu, Lu
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We investigate the galaxy quenching process at intermediate redshift using a sample of $\sim4400$ galaxies with $M_{\ast} > 10^{9}M_{\odot}$ between redshift 0.5 and 1.0 in all five CANDELS fields. We divide this sample, using the integrated specific star formation rate (sSFR), into four sub-groups: star-forming galaxies (SFGs) above and below the ridge of the star-forming main sequence (SFMS), transition galaxies and quiescent galaxies. We study their $UVI$ ($U-V$ versus $V-I$) color gradients to infer their sSFR gradients out to twice effective radii. We show that on average both star-forming and transition galaxies at all masses are not fully quenched at any radii, whereas quiescent galaxies are fully quenched at all radii. We find that at low masses ($M_{\ast} = 10^{9}-10^{10}M_{\odot}$) SFGs both above and below the SFMS ridge generally have flat sSFR profiles, whereas the transition galaxies at the same masses generally have sSFRs that are more suppressed in their outskirts. In contrast, at high masses ($M_{\ast} > 10^{10.5}M_{\odot}$), SFGs above and below the SFMS ridge and transition galaxies generally have varying degrees of more centrally-suppressed sSFRs relative to their outskirts. These findings indicate that at $z\sim~0.5-1.0$ the main galaxy quenching mode depends on its already formed stellar mass, exhibiting a transition from "the outside-in" at $M_{\ast} \leq 10^{10}M_{\odot}$ to "the inside-out" at $M_{\ast} > 10^{10.5}M_{\odot}$. In other words, our findings support that internal processes dominate the quenching of massive galaxies, whereas external processes dominate the quenching of low-mass galaxies., Comment: 19 pages, 12 figures, 2 tables, accepted for publication in ApJ
Published: 2018
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44. Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

Author: Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domínguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., and Tuccillo, D.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a Convolutional Neural Network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution: pre-BN, BN and post-BN, and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of $\sim0.15$ Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at $z=1-3$, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, $10^{9.2-10.3} M_\odot$ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations, and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations., Comment: Accepted for publication in ApJ
Published: 2018
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45. $M_*/L$ gradients driven by IMF variation: Large impact on dynamical stellar mass estimates

Author: Bernardi, M., Sheth, R. K., Dominguez-Sanchez, H., Fischer, J. -L., Chae, K. -H., Huertas-Company, M., and Shankar, F.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Within a galaxy the stellar mass-to-light ratio $\Upsilon_*$ is not constant. Spatially resolved kinematics of nearby early-type galaxies suggest that allowing for a variable initial mass function (IMF) returns significantly larger $\Upsilon_*$ gradients than if the IMF is held fixed. If $\Upsilon_*$ is greater in the central regions, then ignoring the IMF-driven gradient can overestimate $M_*^{\rm dyn}$ by as much as a factor of two for the most massive galaxies, though stellar population estimates $M_*^{\rm SP}$ are also affected. Large $\Upsilon_*$-gradients have four main consequences: First, $M_*^{\rm dyn}$ cannot be estimated independently of stellar population synthesis models. Second, if there is a lower limit to $\Upsilon_*$ and gradients are unknown, then requiring $M_*^{\rm dyn}=M_*^{\rm SP}$ constrains them. Third, if gradients are stronger in more massive galaxies, then $M_*^{\rm dyn}$ and $M_*^{\rm SP}$ can be brought into agreement, not by shifting $M_*^{\rm SP}$ upwards by invoking constant bottom-heavy IMFs, as advocated by a number of recent studies, but by revising $M_*^{\rm dyn}$ estimates in the literature downwards. Fourth, accounting for $\Upsilon_*$ gradients changes the high-mass slope of the stellar mass function $\phi(M_*^{\rm dyn})$, and reduces the associated stellar mass density. These conclusions potentially impact estimates of the need for feedback and adiabatic contraction, so our results highlight the importance of measuring $\Upsilon_*$ gradients in larger samples., Comment: 13 pages, 7 figures, MNRAS in press
Published: 2017
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46. Improving galaxy morphologies for SDSS with Deep Learning

Author: Sánchez, H. Domínguez, Huertas-Company, M., Bernardi, M., Tuccillo, D., and Fischer, J. L.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper., Comment: 18 pages, 21 figures; Accepted for publication in MNRAS
Published: 2017
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47. Deep learning for galaxy surface brightness profile fitting

Author: Tuccillo, D., Huertas-Company, M., Decencière, E., Velasco-Forero, S., Sánchez, H. Domínguez, and Dimauro, P.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies. The full potential of these surveys can only be unlocked with the development of automated, fast and reliable analysis methods. In this paper we present DeepLeGATo, a new method for two-dimensional photometric galaxy profile modeling, based on convolutional neural networks. Our code is trained and validated on analytic profiles (HST/CANDELS F160W filter) and it is able to retrieve the full set of parameters of one- component S\'ersic models: total magnitude, effective radius, S\'ersic index, axis ratio. We show detailed comparisons between our code and GALFIT. On simulated data, our method is more accurate than GALFIT and 3000 time faster on GPU (50 times when run on the same CPU). On real data, DeepLeGATo trained on simulations behaves similarly to GALFIT on isolated galaxies. With a fast domain adaptation step made with the 0.1 - 0.8 per cent the size of the training set, our code is easily capable to reproduce the results obtained with GALFIT even on crowded regions. DeepLeGATo does not require any human intervention beyond the training step, rendering it much automated than traditional profiling methods. The development of this method for more complex models (two-component galaxies, variable PSF, dense sky regions) could constitute a fundamental tool in the era of big data in astronomy., Comment: 17 pages, 9 figures
Published: 2017
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48. Stellar mass functions and implications for a variable IMF

Author: Bernardi, M., Sheth, R. K., Fischer, J. -L., Meert, A., Chae, K. -H., Dominguez-Sanchez, H., Huertas-Company, M., Shankar, F., and Vikram, V.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: Spatially resolved kinematics of nearby galaxies has shown that the ratio of dynamical- to stellar population-based estimates of the mass of a galaxy ($M_*^{\rm JAM}/M_*$) correlates with $\sigma_e$, if $M_*$ is estimated using the same IMF for all galaxies and the stellar M/L ratio within each galaxy is constant. This correlation may indicate that, in fact, the IMF is more dwarf-rich for galaxies with large $\sigma$. We use this correlation to estimate a dynamical or IMF-corrected stellar mass, $M_*^{\rm \alpha_{JAM}}$, from $M_{*}$ and $\sigma_e$ for a sample of $6 \times 10^5$ SDSS galaxies for which spatially resolved kinematics is not available. We also compute the `virial' mass estimate $k(n,R)\,R_e\,\sigma_R^2/G$, where $n$ is the Sersic index, in the SDSS and ATLAS$^{\rm 3D}$ samples. We show that an $n$-dependent correction must be applied to the $k(n,R)$ values provided by Prugniel & Simien (1997). Our analysis also shows that the shape of the velocity dispersion profile in the ATLAS$^{\rm 3D}$ sample varies weakly with $n$: $(\sigma_R/\sigma_e) = (R/R_e)^{-\gamma(n)}$. The resulting stellar mass functions, based on $M_*^{\rm \alpha_{JAM}}$ and the recalibrated virial mass, are in good agreement. If the $M_*^{\rm \alpha_{JAM}}/M_* - \sigma_e$ correlation is indeed due to the IMF, and stellar M/L gradients can be ignored, then our $\phi(M_*^{\rm \alpha_{JAM}})$ is an estimate of the stellar mass function in which $\sigma_e$-dependent variations in the IMF across the population have been accounted for. Using a Fundamental Plane based observational proxy for $\sigma_e$ produces comparable results. By demonstrating that cheaper proxies are sufficiently accurate, our analysis should enable a more reliable census of the mass in stars for large galaxy samples, at a fraction of the cost. Our results are provided in tabular form., Comment: 17 pages, 19 figures, 4 tables. Accepted for publication by MNRAS. Tables 1, C1 and C2 are provided as ancillary files
Published: 2017
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49. The Morphological Transformation of Red Sequence Galaxies in Clusters since $z \sim 1$

Author: Cerulo, P., Couch, W. J., Lidman, C., Demarco, R., Huertas-Company, M., Mei, S., Sánchez-Janssen, R., Barrientos, L. F., and Muñoz, R. P.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The study of galaxy morphology is fundamental to understand the physical processes driving the structural evolution of galaxies. It has long been known that dense environments host high fractions of early-type galaxies and low fractions of late-type galaxies, indicating that the environment affects the structural evolution of galaxies. In this paper we present an analysis of the morphological composition of red sequence galaxies in a sample of 9 galaxy clusters at $0.8
Published: 2017
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50. Comparing PyMorph and SDSS photometry. II. The differences are more than semantics and are not dominated by intracluster light

Author: Bernardi, M., Fischer, J. -L., Sheth, R. K., Meert, A., Huertas-Company, M., Shankar, F., and Vikram, V.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The Sloan Digital Sky Survey pipeline photometry underestimates the brightnesses of the most luminous galaxies. This is mainly because (i) the SDSS overestimates the sky background and (ii) single or two-component Sersic-based models better fit the surface brightness profile of galaxies, especially at high luminosities, than does the de Vaucouleurs model used by the SDSS pipeline. We use the PyMorph photometric reductions to isolate effect (ii) and show that it is the same in the full sample as in small group environments, and for satellites in the most massive clusters as well. None of these are expected to be significantly affected by intracluster light (ICL). We only see an additional effect for centrals in the most massive halos, but we argue that even this is not dominated by ICL. Hence, for the vast majority of galaxies, the differences between PyMorph and SDSS pipeline photometry cannot be ascribed to the semantics of whether or not one includes the ICL when describing the stellar mass of massive galaxies. Rather, they likely reflect differences in star formation or assembly histories. Failure to account for the SDSS underestimate has significantly biased most previous estimates of the SDSS luminosity and stellar mass functions, and therefore Halo Model estimates of the z ~ 0.1 relation between the mass of a halo and that of the galaxy at its center. We also show that when one studies correlations, at fixed group mass, with a quantity which was not used to define the groups, then selection effects appear. We show why such effects arise, and should not be mistaken for physical effects., Comment: 15 pages, 17 figures, accepted for publication in MNRAS. The PyMorph luminosities and stellar masses are available at https://www.physics.upenn.edu/~ameert/SDSS_PhotDec/
Published: 2017
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484 results on '"Huertas-Company, M"'

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