{"paper":{"title":"Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.GA","authors_text":"A. Amara, A. Balaguera-Antol\\'inez, A. Biviano, A. Boucaud, A. Cappi, A. Cimatti, A. Costille, A. Da Silva, A. Grazian, A. G. S\\'anchez, A. Kashlinsky, A. Kiessling, A. N. Taylor, A. Nucita, A. Renzi, A. Secroun, A. Tramacere, A. Zacchei, B. Garilli, B. Gillis, B. Joachimi, B. Morin, C .A. J. Duncan, C. Baccigalupi, C. Bodendorf, C. Burigana, C. Carbone, C. C. Kirkpatrick, C. Colodro-Conde, C. Giocoli, C. J. Conselice, C. Padilla, C. S. Carvalho, C. Sirignano, D. Bonino, D. Guinet, D. Maino, D. Potter, D. Sapone, D. Tuccillo, E. A. Valentijn, E. Bozzo, E. Branchini, E. Franceschi, E. Gaztanaga, E. Jullo, E. Keihanen, E. Maiorano, E. Medinaceli, E. Merlin, E. Munari, E. Romelli, E. Rossetti, E. Sefusatti, E. Soubrie, Euclid Collaboration: H. Bretonni\\`ere, E. Zucca, F. Dubath, F. Grupp, F. Hormuth, F. J. Castander, F. Lanusse, F. Marulli, F. Pasian, F. Raison, F. Torradeflot, G. Castignani, G. Congedo, G. Gozaliasl, G. Mainetti, G. Meylan, G. Morgante, G. Polenta, G. Riccio, G. Seidel, G. Sirri, G. Zamorani, H. Degaudenzi, H. Dole, H. J. McCracken, H. Kurki-Suonio, H. M.Courtois, I. Lloro, I. M. Hook, I. Tereno, I. Tutusaus, J. Brinchmann, J. Carretero, J. Coupon, J. Garcia-Bellido, J.H Knapen, J. -L. Starck, J. Nightingale, J. Rhodes, J. Valiviita, J. Weller, J. Zoubian, K. Ganga, K. Jahnke, K. Markovic, K. Pedersen, L. Conversi, L. Corcione, L. Moscardini, L. Patrizii, L. Popa, L. Pozzetti, L. Stanco, L. Valenziano, L. Wang, L. Whittaker, M. Baldi, M. Brescia, M. Castellano, M. Douspis, M. Fabricius, M. Farina, M. Frailis, M. Fumana, M. Huertas-Company, M. Kilbinger, M. K\\\"ummel, M. Kunz, M. Martinelli, M. Melchior, M. Meneghetti, M. Moresco, M. Poncet, M. Roncarelli, M. Schirmer, M. Schultheis, M. Tenti, M. Viel, N. Auricchio, N. Martinet, N. Morisset, N. Welikala, O. Mansutti, O. Marggraf, P. A. Duc, P. B. Lilje, P. Flose-Reimberg, P. Fosalba, P. G. Ferreira, P. Hudelot, P. Schneider, P. Tallada-Cresp\\'i, R. Bender, R. B. Metcalf, R. Cabanac, R. Cledassou, R. Farinelli, R. Kohley, R. Maoli, R. Massey, R. Nakajima, R. Rebolo, R. Saglia, R. Teyssier, R. Toledo-Moreo, S. Bardelli, S. Borgani, S. Brau-Nogue, S. Camera, S. Casas, S. Cavuoti, S. de la Torre, S. Dusini, S. Farrens, S. Ferriol, S. Fotopoulou, S. Galeotta, S. Kermiche, S. Kruk, S. Ligori, S. Maurogordato, S. Mei, S. M. Niemi, S. Paltani, S. Pires, S. Serrano, S. V. H. Haugan, T. Kitching, T. Vassallo, U. Kuchner, V. Capobianco, V. Kansal, V. Lindholm, V. Pettorino, V. Scottez, W. Gillard, W. Holmes, X. Dupac, Y. Copin, Y. Wang","submitted_at":"2021-05-25T18:00:07Z","abstract_excerpt":"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 Surv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12149","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2105.12149/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}