@article{7d79c709f8a04ae09a434545670a353e,
title = "Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models",
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 S{\'e}rsic 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 1010.6 M⊙ (resp. 109.6 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.",
keywords = "cosmology: observations, galaxies: evolution, galaxies: structure, surveys, techniques: image processing",
author = "{Euclid Collaboration} and H. Bretonni{\`e}re and M. Huertas-Company and A. Boucaud and F. Lanusse and E. Jullo and E. Merlin and D. Tuccillo and M. Castellano and J. Brinchmann and Conselice, {C. J.} and H. Dole and R. Cabanac and Courtois, {H. M.} and Castander, {F. J.} and Duc, {P. A.} and P. Fosalba and D. Guinet and S. Kruk and U. Kuchner and S. Serrano and E. Soubrie and A. Tramacere and L. Wang and A. Amara and N. Auricchio and R. Bender and C. Bodendorf and D. Bonino and E. Branchini and S. Brau-Nogue and M. Brescia and V. Capobianco and C. Carbone and J. Carretero and S. Cavuoti and A. Cimatti and R. Cledassou and G. Congedo and L. Conversi and Y. Copin and L. Corcione and A. Costille and M. Cropper and {Da Silva}, A. and H. Degaudenzi and M. Douspis and F. Dubath and Duncan, {C. A. J.} and X. Dupac and S. Dusini",
note = "Funding Information: Acknowledgements. 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 Informa-tique de l{\textquoteright}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{\textquoteright}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{\textquoteright}Informaci{\'o} Cient{\'i}fica (PIC), maintained through a collaboration of the Institut de F{\'i}sica d{\textquoteright}Altes Energies (IFAE) and the Centro de Investigaciones Energ{\'e}ticas, Medioambientales y Tecnol{\'o}gi-cas (CIEMAT) and the Institute of Space Sciences (CSIC and IEEC), and was partially funded by the “Plan Estatal de Investigaci{\'o}n Cient{\'i}fica y T{\'e}cnica y de Innovaci{\'o}n” 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{\textquoteright}Etudes Spatiales, the Deutsches Zentrum f{\"u}r Luft-und Raumfahrt, the Danish Space Research Institute, the Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, the Ministerio de Economia y Com-petitividad, the National Aeronautics and Space Administration, the Nether-landse 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). Publisher Copyright: {\textcopyright} Euclid Collaboration 2022.",
year = "2022",
month = jan,
day = "18",
doi = "10.1051/0004-6361/202141393",
language = "English",
volume = "657",
journal = "Astronomy and Astrophysics",
issn = "0004-6361",
publisher = "EDP Sciences",
}