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Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

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arxiv 2105.12149 v3 pith:5FKWD5RH submitted 2021-05-25 astro-ph.GA

Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

Euclid Collaboration: H. Bretonni\`ere , M. Huertas-Company , A. Boucaud , F. Lanusse , E. Jullo , E. Merlin , D. Tuccillo , M. Castellano
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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 V. Capobianco C. Carbone J. Carretero S. Cavuoti A. Cimatti R. Cledassou L. Corcione A. Costille H. Degaudenzi M. Douspis F. Dubath S. Dusini S. Ferriol M. Frailis E. Franceschi M. Fumana B. Garilli C. Giocoli A. Grazian F. Grupp S. V. H. Haugan W. Holmes F. Hormuth P. Hudelot K. Jahnke A. Kiessling M. Kilbinger T. Kitching M. K\"ummel M. Kunz H. Kurki-Suonio S. Ligori P. B. Lilje I. Lloro E. Maiorano O. Mansutti O. Marggraf K. Markovic R. Massey M. Melchior M. Meneghetti G. Meylan L. Moscardini 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 J. -L. Starck A. N. Taylor I. Tereno R. Toledo-Moreo E. A. Valentijn L. Valenziano Y. Wang J. Weller G. Zamorani J. Zoubian M. Baldi S. Bardelli S. Brau-Nogue M. Brescia S. Camera G. Congedo L. Conversi Y. Copin C .A. J. Duncan X. Dupac R. Farinelli B. Gillis S. Kermiche R. Kohley F. Marulli E. Medinaceli S. Mei M. Moresco B. Morin E. Munari G. Polenta E. Romelli P. Tallada-Cresp\'i M. Tenti F. Torradeflot T. Vassallo N. Welikala A. Zacchei E. Zucca C. Baccigalupi A. Balaguera-Antol\'inez A. Biviano S. Borgani E. Bozzo C. Burigana A. Cappi C. S. Carvalho S. Casas G. Castignani C. Colodro-Conde J. Coupon A. Da Silva S. de la Torre M. Fabricius M. Farina S. Farrens P. G. Ferreira P. Flose-Reimberg S. Fotopoulou S. Galeotta K. Ganga J. Garcia-Bellido E. Gaztanaga W. Gillard 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 S. Maurogordato H. J. McCracken R. B. Metcalf G. Morgante N. Morisset R. Nakajima J. Nightingale A. Nucita L. Patrizii D. Potter A. Renzi G. Riccio A. G. S\'anchez D. Sapone M. Schirmer M. Schultheis V. Scottez E. Sefusatti L. Stanco R. Teyssier I. Tutusaus J. Valiviita M. Viel L. Whittaker J.H Knapen
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classification astro-ph.GA
keywords euclidsurveydeepgalaxiesgalaxymodelsrealisticanalytic
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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 Survey will be able to resolve the internal morphological structure of galaxies down to a surface brightness of $22.5\,\rm{mag}\,\rm{arcsec}^{-2}$, and $24.9\,\rm{mag}\,\rm{arcsec}^{-2}$ for the Euclid Deep Survey. This corresponds to approximately $250$ million galaxies at the end of the mission and a $50\,\%$ complete sample for stellar masses above $10^{10.6}\,\rm{M}_\odot$ (resp. $10^{9.6}\,\rm{M}_\odot$) at a redshift $z\sim0.5$ for the wide (resp. deep) survey. 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.

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