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

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.; Silva, A. Da; 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.

doi:10.1051/0004-6361/202141393

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

Bretonnière, H., Huertas-Company, M., Boucaud, A., Lanusse, F., Jullo, E., Merlin, E., et al. (2022). Euclid preparation - XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models. Astronomy and Astrophysics, 657: A90. doi:10.1051/0004-6361/202141393.

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Bretonnière, H., Author
Huertas-Company, M., Author
Boucaud, A., Author
Lanusse, F., Author
Jullo, E., Author
Merlin, E., Author
Tuccillo, D., Author
Castellano, M., Author
Brinchmann, J., Author
Conselice, C. J., Author
Dole, H., Author
Cabanac, R., Author
Courtois, H. M., Author
Castander, F. J., Author
Duc, P. A., Author
Fosalba, P., Author
Guinet, D., Author
Kruk, S., Author
Kuchner, U., Author
Serrano, S., Author
Soubrie, E., AuthorTramacere, A., AuthorWang, L., AuthorAmara, A., AuthorAuricchio, N., AuthorBender, R.¹, Author Bodendorf, C.¹, Author Bonino, D., AuthorBranchini, E., AuthorBrau-Nogue, S., AuthorBrescia, M., AuthorCapobianco, V., AuthorCarbone, C., AuthorCarretero, J., AuthorCavuoti, S., AuthorCimatti, A., AuthorCledassou, R., AuthorCongedo, G., AuthorConversi, L., AuthorCopin, Y., AuthorCorcione, L., AuthorCostille, A., AuthorCropper, M., AuthorSilva, A. Da, AuthorDegaudenzi, H., AuthorDouspis, M., AuthorDubath, F., AuthorDuncan, C. A. J., AuthorDupac, X., AuthorDusini, S., AuthorFarrens, S., AuthorFerriol, S., AuthorFrailis, M., AuthorFranceschi, E., AuthorFumana, M., AuthorGarilli, B., AuthorGillard, W., AuthorGillis, B., AuthorGiocoli, C., AuthorGrazian, A., AuthorGrupp, F.¹, Author Haugan, S. V. H., AuthorHolmes, W., AuthorHormuth, F., AuthorHudelot, P., AuthorJahnke, K., AuthorKermiche, S., AuthorKiessling, A., AuthorKilbinger, M., AuthorKitching, T., AuthorKohley, R., AuthorKümmel, M., AuthorKunz, M., AuthorKurki-Suonio, H., AuthorLigori, S., AuthorLilje, P. B., AuthorLloro, I., AuthorMaiorano, E., AuthorMansutti, O., AuthorMarggraf, O., AuthorMarkovic, K., AuthorMarulli, F., AuthorMassey, R., AuthorMaurogordato, S., AuthorMelchior, M., AuthorMeneghetti, M., AuthorMeylan, G., AuthorMoresco, M., AuthorMorin, B., AuthorMoscardini, L., AuthorMunari, E., AuthorNakajima, R., AuthorNiemi, S. M., AuthorPadilla, C., AuthorPaltani, S., AuthorPasian, F., AuthorPedersen, K., AuthorPettorino, V., AuthorPires, S., AuthorPoncet, M., AuthorPopa, L., AuthorPozzetti, L., AuthorRaison, F.¹, Author Rebolo, R., AuthorRhodes, J., AuthorRoncarelli, M., AuthorRossetti, E., AuthorSaglia, R., AuthorSchneider, P., AuthorSecroun, A., AuthorSeidel, G., AuthorSirignano, C., AuthorSirri, G., AuthorStanco, L., AuthorStarck, J.-L., AuthorTallada-Crespí, P., AuthorTaylor, A. N., AuthorTereno, I., AuthorToledo-Moreo, R., AuthorTorradeflot, F., AuthorValentijn, E. A., AuthorValenziano, L., AuthorWang, Y., AuthorWelikala, N., AuthorWeller, J.¹, Author Zamorani, G., AuthorZoubian, J., AuthorBaldi, M., AuthorBardelli, S., AuthorCamera, S., AuthorFarinelli, R., AuthorMedinaceli, E., AuthorMei, S., AuthorPolenta, G., AuthorRomelli, E., AuthorTenti, M., AuthorVassallo, T., AuthorZacchei, A., AuthorZucca, E., AuthorBaccigalupi, C., AuthorBalaguera-Antolínez, A., AuthorBiviano, A., AuthorBorgani, S., AuthorBozzo, E., AuthorBurigana, C., AuthorCappi, A., AuthorCarvalho, C. S., AuthorCasas, S., AuthorCastignani, G., AuthorColodro-Conde, C., AuthorCoupon, J., Authorde la Torre, S., AuthorFabricius, M.¹, Author Farina, M., AuthorFerreira, P. G., AuthorFlose-Reimberg, P., AuthorFotopoulou, S., AuthorGaleotta, S., AuthorGanga, K., AuthorGarcia-Bellido, J., AuthorGaztanaga, E., AuthorGozaliasl, G., AuthorHook, I. M., AuthorJoachimi, B., AuthorKansal, V., AuthorKashlinsky, A., AuthorKeihanen, E., AuthorKirkpatrick, C. C., AuthorLindholm, V., AuthorMainetti, G., AuthorMaino, D., AuthorMaoli, R., AuthorMartinelli, M., AuthorMartinet, N., AuthorMcCracken, H. J., AuthorMetcalf, R. B., AuthorMorgante, G., AuthorMorisset, N., AuthorNightingale, J., AuthorNucita, A., AuthorPatrizii, L., AuthorPotter, D., AuthorRenzi, A., AuthorRiccio, G., AuthorSánchez, A. G.¹, Author Sapone, D., AuthorSchirmer, M., AuthorSchultheis, M., AuthorScottez, V., AuthorSefusatti, E., AuthorTeyssier, R., AuthorTutusaus, I., AuthorValiviita, J., AuthorViel, M., AuthorWhittaker, L., AuthorKnapen, J. H., Author more..

Affiliations:
1Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society, ou_159895

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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.

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Language(s): eng - English

Dates: Published Online: 2022-01-18

Publication Status: Published online

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Identifiers: DOI: 10.1051/0004-6361/202141393

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Title: Astronomy and Astrophysics

Other : Astron. Astrophys.

Source Genre: Journal

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Publ. Info: France : EDP Sciences S A

Pages: - Volume / Issue: 657 Sequence Number: A90 Start / End Page: - Identifier: ISSN: 1432-0746
CoNE: https://pure.mpg.de/cone/journals/resource/954922828219_1