Title: | Euclid preparation. XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models |
Authors: | Euclid Collaboration Bretonnière, H. Huertas-Company, M. Boucaud, A. Lanusse, F. Jullo, E. MERLIN, Emiliano Tuccillo, D. CASTELLANO, MARCO Brinchmann, J. Conselice, C. J. Poncet, M. Popa, L. POZZETTI, Lucia Raison, F. Rebolo, R. Rhodes, J. Roncarelli, M. Rossetti, E. Saglia, R. Schneider, P. Dole, H. Secroun, A. Seidel, G. Sirignano, C. Sirri, G. Stanco, L. Starck, J. -L. Tallada-Crespí, P. Taylor, A. N. Tereno, I. Toledo-Moreo, R. Cabanac, R. Torradeflot, F. Valentijn, E. A. VALENZIANO, LUCA Wang, Y. Welikala, N. Weller, J. Zamorani, G. Zoubian, J. Baldi, M. BARDELLI, Sandro Courtois, H. M. Camera, S. FARINELLI, Ruben Medinaceli, E. Mei, S. Polenta, G. Romelli, Erik Tenti, M. Vassallo, T. ZACCHEI, Andrea ZUCCA, Elena Castander, F. J. Baccigalupi, C. Balaguera-Antolínez, A. BIVIANO, ANDREA BORGANI, STEFANO Bozzo, E. BURIGANA, CARLO CAPPI, Alberto Carvalho, C. S. Casas, S. Castignani, G. Duc, P. A. Colodro-Conde, C. Coupon, J. de la Torre, S. Fabricius, M. FARINA, Maria Ferreira, P. G. Flose-Reimberg, P. Fotopoulou, S. GALEOTTA, Samuele Ganga, K. Fosalba, P. Garcia-Bellido, J. Gaztanaga, E. Gozaliasl, G. Hook, I. M. Joachimi, B. Kansal, V. Kashlinsky, A. Keihanen, E. Kirkpatrick, C. C. Lindholm, V. Guinet, D. Mainetti, G. Maino, D. Maoli, R. Martinelli, M. Martinet, N. McCracken, H. J. Metcalf, R. B. MORGANTE, GIANLUCA Morisset, N. Nightingale, J. Kruk, S. Nucita, A. Patrizii, L. Potter, D. Renzi, A. RICCIO, GIUSEPPE Sánchez, A. G. Sapone, D. Schirmer, M. Schultheis, M. Scottez, V. Kuchner, U. SEFUSATTI, Emiliano Teyssier, R. Tutusaus, I. Valiviita, J. VIEL, MATTEO Whittaker, L. Knapen, J. H. Serrano, S. Soubrie, E. Tramacere, A. Wang, L. Amara, A. AURICCHIO, NATALIA Bender, R. Bodendorf, C. BONINO, Donata Branchini, Enzo Brau-Nogue, S. BRESCIA, Massimo Capobianco, Vito CARBONE, Carmelita Carretero, J. CAVUOTI, STEFANO Cimatti, A. Cledassou, R. Congedo, G. Conversi, L. Copin, Y. CORCIONE, Leonardo Costille, A. Cropper, M. Da Silva, A. Degaudenzi, H. Douspis, M. Dubath, F. Duncan, C. A. J. Dupac, X. Dusini, S. Farrens, S. Ferriol, S. FRAILIS, Marco FRANCESCHI, ENRICO FUMANA, Marco GARILLI, BIANCA MARIA ROSA Gillard, W. Gillis, B. GIOCOLI, Carlo GRAZIAN, Andrea Grupp, F. Haugan, S. V. H. Holmes, W. Hormuth, F. Hudelot, P. Jahnke, K. Kermiche, S. Kiessling, A. Kilbinger, M. Kitching, T. Kohley, R. Kümmel, M. Kunz, M. Kurki-Suonio, H. LIGORI, Sebastiano Lilje, P. B. Lloro, I. MAIORANO, Elisabetta MANSUTTI, Oriana Marggraf, O. Markovic, K. Marulli, F. Massey, R. Maurogordato, S. Melchior, M. MENEGHETTI, MASSIMO Meylan, G. Moresco, M. Morin, B. Moscardini, L. Munari, Emiliano Nakajima, R. Niemi, S. M. Padilla, C. Paltani, S. Pasian, F. Pedersen, K. Pettorino, V. Pires, S. |
Issue Date: | 2022 |
Journal: | ASTRONOMY & ASTROPHYSICS |
Number: | 657 |
First Page: | A90 |
Abstract: | We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid. 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 deg<SUP>2</SUP> as it will be seen by the Euclid visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide Survey (EWS) will be able to resolve the internal morphological structure of galaxies down to a surface brightness of 22.5 mag arcsec<SUP>−2</SUP>, and the Euclid Deep Survey (EDS) down to 24.9 mag arcsec<SUP>−2</SUP>. This corresponds to approximately 250 million galaxies at the end of the mission and a 50% complete sample for stellar masses above 10<SUP>10.6</SUP> M<SUB>⊙</SUB> (resp. 10<SUP>9.6</SUP> M<SUB>⊙</SUB>) at a redshift z ∼ 0.5 for the EWS (resp. EDS). 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. |
URI: | http://hdl.handle.net/20.500.12386/32061 |
URL: | http://arxiv.org/abs/2105.12149v3 https://www.aanda.org/articles/aa/full_html/2022/01/aa41393-21/aa41393-21.html |
ISSN: | 0004-6361 |
DOI: | 10.1051/0004-6361/202141393 |
Bibcode ADS: | 2022A&A...657A..90E |
Fulltext: | open |
Appears in Collections: | 1.01 Articoli in rivista
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