Euclid preparation. XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

Creators: 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.; Brau-Nogue, S.; Brescia, M.; Capobianco, V.; Carbone, C.; Carretero, J.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conversi, L.; Copin, Y.; Corcione, L.; Costille, A.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Douspis, M.; Dubath, F.; Duncan, C. A. J.; Dupac, X.; Dusini, S.; Farrens, S.; Ferriol, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Garilli, B.; Gillard, W.; Gillis, B.; Giocoli, C.; Grazian, A.; 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, S.; Lilje, P. B.; Lloro, I.; Maiorano, E.; Mansutti, O.; Marggraf, O.; Markovic, K.; Marulli, F.; Massey, R.; Maurogordato, S.; Melchior, M.; Meneghetti, M.; Meylan, G.; Moresco, M.; Morin, B.; Moscardini, L.; Munari, E.; Nakajima, R.; 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.; Stanco, L.; Starck, J.-L.; Tallada-Crespí, P.; Taylor, A. N.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Valentijn, E. A.; Valenziano, L.; Wang, Y.; Welikala, N.; Weller, J.; Zamorani, G.; Zoubian, J.; Baldi, M.; Bardelli, S.; Camera, S.; Farinelli, R.; Medinaceli, E.; Mei, S.; Polenta, G.; Romelli, E.; Tenti, M.; Vassallo, T.; 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.; de la Torre, S.; Fabricius, M.; Farina, M.; Ferreira, P. G.; Flose-Reimberg, P.; Fotopoulou, S.; Galeotta, S.; Ganga, K.; Garcia-Bellido, J.; Gaztanaga, E.; 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.; McCracken, H. J.; Metcalf, R. B.; Morgante, G.; Morisset, N.; 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.; Teyssier, R.; Tutusaus, I.; Valiviita, J.; Viel, M.; Whittaker, L.; Knapen, J. H.; Euclid Collaboration

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 deg² 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⁻², and the Euclid Deep Survey (EDS) down to 24.9 mag arcsec⁻². 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) M_⊙ (resp. 10^(9.6) M_⊙) 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.

Additional Information

© Euclid Collaboration 2022. Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: 26 May 2021 Accepted: 21 October 2021. We thank the IAC where the first author was in long term visit during the production of this paper, with a special thanks to the TRACES team for their support. We would also like to thank the Direction Informatique de l'Observatoire (DIO) of the Paris Meudon Observatory for the management and support of the GPU we used to train our deep learning models. We also thank the Centre National d'Etudes Spatiales (CNES) and the Centre National de la Recherche Scientifique (CNRS) for the financial support of the PhD in which this study took place. This work has made use of CosmoHub. CosmoHub has been developed by the Port d'Informació Científica (PIC), maintained through a collaboration of the Institut de Física d'Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) and the Institute of Space Sciences (CSIC and IEEC), and was partially funded by the "Plan Estatal de Investigación Científica y Técnica y de Innovación" program of the Spanish government. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d'Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org). Softwares: Astropy (Astropy Collaboration 2013, 2018), GalSim (Rowe et al. 2015), IPython (Perez & Granger 2007), Jupyter (Kluyver et al. 2016), Matplotlib (Hunter 2007), Numpy (Harris et al. 2020), TensorFlow (Abadi et al. 2016), TensorFlow Probability (Dillon et al. 2017).

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