CaltechAUTHORS
  A Caltech Library Service

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

Bretonnière, H. and Huertas-Company, M. and Boucaud, A. and Lanusse, F. and Jullo, E. and Merlin, E. and Tuccillo, D. and Castellano, M. and Brinchmann, J. and Conselice, C. J. and Dole, H. and Cabanac, R. and Courtois, H. M. and Castander, F. J. and Duc, P. A. and Fosalba, P. and Guinet, D. and Kruk, S. and Kuchner, U. and Serrano, S. and Soubrie, E. and Tramacere, A. and Wang, L. and Amara, A. and Auricchio, N. and Bender, R. and Bodendorf, C. and Bonino, D. and Branchini, E. and Brau-Nogue, S. and Brescia, M. and Capobianco, V. and Carbone, C. and Carretero, J. and Cavuoti, S. and Cimatti, A. and Cledassou, R. and Congedo, G. and Conversi, L. and Copin, Y. and Corcione, L. and Costille, A. and Cropper, M. and Da Silva, A. and Degaudenzi, H. and Douspis, M. and Dubath, F. and Duncan, C. A. J. and Dupac, X. and Dusini, S. and Farrens, S. and Ferriol, S. and Frailis, M. and Franceschi, E. and Fumana, M. and Garilli, B. and Gillard, W. and Gillis, B. and Giocoli, C. and Grazian, A. and Grupp, F. and Haugan, S. V. H. and Holmes, W. and Hormuth, F. and Hudelot, P. and Jahnke, K. and Kermiche, S. and Kiessling, A. and Kilbinger, M. and Kitching, T. and Kohley, R. and Kümmel, M. and Kunz, M. and Kurki-Suonio, H. and Ligori, S. and Lilje, P. B. and Lloro, I. and Maiorano, E. and Mansutti, O. and Marggraf, O. and Markovic, K. and Marulli, F. and Massey, R. and Maurogordato, S. and Melchior, M. and Meneghetti, M. and Meylan, G. and Moresco, M. and Morin, B. and Moscardini, L. and Munari, E. and Nakajima, R. and Niemi, S. M. and Padilla, C. and Paltani, S. and Pasian, F. and Pedersen, K. and Pettorino, V. and Pires, S. and Poncet, M. and Popa, L. and Pozzetti, L. and Raison, F. and Rebolo, R. and Rhodes, J. and Roncarelli, M. and Rossetti, E. and Saglia, R. and Schneider, P. and Secroun, A. and Seidel, G. and Sirignano, C. and Sirri, G. and Stanco, L. and Starck, J.-L. and Tallada-Crespí, P. and Taylor, A. N. and Tereno, I. and Toledo-Moreo, R. and Torradeflot, F. and Valentijn, E. A. and Valenziano, L. and Wang, Y. and Welikala, N. and Weller, J. and Zamorani, G. and Zoubian, J. and Baldi, M. and Bardelli, S. and Camera, S. and Farinelli, R. and Medinaceli, E. and Mei, S. and Polenta, G. and Romelli, E. and Tenti, M. and Vassallo, T. and Zacchei, A. and Zucca, E. and Baccigalupi, C. and Balaguera-Antolínez, A. and Biviano, A. and Borgani, S. and Bozzo, E. and Burigana, C. and Cappi, A. and Carvalho, C. S. and Casas, S. and Castignani, G. and Colodro-Conde, C. and Coupon, J. and de la Torre, S. and Fabricius, M. and Farina, M. and Ferreira, P. G. and Flose-Reimberg, P. and Fotopoulou, S. and Galeotta, S. and Ganga, K. and Garcia-Bellido, J. and Gaztanaga, E. and Gozaliasl, G. and Hook, I. M. and Joachimi, B. and Kansal, V. and Kashlinsky, A. and Keihanen, E. and Kirkpatrick, C. C. and Lindholm, V. and Mainetti, G. and Maino, D. and Maoli, R. and Martinelli, M. and Martinet, N. and McCracken, H. J. and Metcalf, R. B. and Morgante, G. and Morisset, N. and Nightingale, J. and Nucita, A. and Patrizii, L. and Potter, D. and Renzi, A. and Riccio, G. and Sánchez, A. G. and Sapone, D. and Schirmer, M. and Schultheis, M. and Scottez, V. and Sefusatti, E. and Teyssier, R. and Tutusaus, I. and Valiviita, J. and Viel, M. and Whittaker, L. and Knapen, J. H. (2022) Euclid preparation. XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models. Astronomy and Astrophysics, 657 . Art. No. A90. ISSN 0004-6361. doi:10.1051/0004-6361/202141393. https://resolver.caltech.edu/CaltechAUTHORS:20220204-680070000

[img] PDF - Published Version
Creative Commons Attribution.

3MB
[img] PDF - Accepted Version
Creative Commons Attribution.

3MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20220204-680070000

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1051/0004-6361/202141393DOIArticle
https://arxiv.org/abs/2105.12149arXivDiscussion Paper
ORCID:
AuthorORCID
Boucaud, A.0000-0001-7387-2633
Lanusse, F.0000-0001-7956-0542
Jullo, E.0000-0002-9253-053X
Merlin, E.0000-0001-6870-8900
Castellano, M.0000-0001-9875-8263
Brinchmann, J.0000-0003-4359-8797
Castander, F. J.0000-0001-7316-4573
Meneghetti, M.0000-0003-1225-7084
Rhodes, J.0000-0002-4485-8549
Alternate Title:Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models
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).
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
Centre National d'Études Spatiales (CNES)UNSPECIFIED
Centre National de la Recherche Scientifique (CNRS)UNSPECIFIED
Plan Estatal de Investigación Científica y Técnica y de InnovaciónUNSPECIFIED
European Space Agency (ESA)UNSPECIFIED
Academy of FinlandUNSPECIFIED
Agenzia Spaziale Italiana (ASI)UNSPECIFIED
Belgian Federal Science Policy Office (BELSPO)UNSPECIFIED
Canadian Euclid ConsortiumUNSPECIFIED
Deutsches Zentrum für Luft- und Raumfahrt (DLR)UNSPECIFIED
Danish Space Research InstituteUNSPECIFIED
Fundação para a Ciência e a Tecnologia (FCT)UNSPECIFIED
Ministerio de Economia y Competitividad (MINECO)UNSPECIFIED
NASAUNSPECIFIED
Nederlandse Onderzoekschool voor de Astronomie (NOVA)UNSPECIFIED
Norwegian Space AgencyUNSPECIFIED
Romanian Space AgencyUNSPECIFIED
Swiss Space Office (SSO)UNSPECIFIED
United Kingdom Space Agency (UKSA)UNSPECIFIED
Subject Keywords:techniques: image processing / surveys / galaxies: structure / galaxies: evolution / cosmology: observations
DOI:10.1051/0004-6361/202141393
Record Number:CaltechAUTHORS:20220204-680070000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220204-680070000
Official Citation:Euclid preparation - XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models Euclid Collaboration, H. Bretonnière, M. Huertas-Company, A. Boucaud, F. Lanusse, E. Jullo, E. Merlin, D. Tuccillo, M. Castellano, J. Brinchmann, C. J. Conselice, H. Dole, R. Cabanac, H. M. Courtois, F. J. Castander, P. A. Duc, P. Fosalba, D. Guinet, S. Kruk, U. Kuchner, S. Serrano, E. Soubrie, A. Tramacere, L. Wang, A. Amara, N. Auricchio, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, S. Brau-Nogue, M. Brescia, V. Capobianco, C. Carbone, J. Carretero, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, A. Costille, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, B. Morin, L. Moscardini, E. Munari, R. Nakajima, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, J. Rhodes, M. Roncarelli, E. Rossetti, R. Saglia, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, J.-L. Starck, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, E. A. Valentijn, L. Valenziano, Y. Wang, N. Welikala, J. Weller, G. Zamorani, J. Zoubian, M. Baldi, S. Bardelli, S. Camera, R. Farinelli, E. Medinaceli, S. Mei, G. Polenta, E. Romelli, M. Tenti, T. Vassallo, A. Zacchei, E. Zucca, C. Baccigalupi, A. Balaguera-Antolínez, A. Biviano, S. Borgani, E. Bozzo, C. Burigana, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, C. Colodro-Conde, J. Coupon, S. de la Torre, M. Fabricius, M. Farina, P. G. Ferreira, P. Flose-Reimberg, S. Fotopoulou, S. Galeotta, K. Ganga, J. Garcia-Bellido, E. Gaztanaga, G. Gozaliasl, I. M. Hook, B. Joachimi, V. Kansal, A. Kashlinsky, E. Keihanen, C. C. Kirkpatrick, V. Lindholm, G. Mainetti, D. Maino, R. Maoli, M. Martinelli, N. Martinet, H. J. McCracken, R. B. Metcalf, G. Morgante, N. Morisset, J. Nightingale, A. Nucita, L. Patrizii, D. Potter, A. Renzi, G. Riccio, A. G. Sánchez, D. Sapone, M. Schirmer, M. Schultheis, V. Scottez, E. Sefusatti, R. Teyssier, I. Tutusaus, J. Valiviita, M. Viel, L. Whittaker and J. H. Knapen A&A, 657 (2022) A90 DOI: https://doi.org/10.1051/0004-6361/202141393
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113291
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Feb 2022 17:28
Last Modified:07 Feb 2022 17:28

Repository Staff Only: item control page