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

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Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models. / Euclid Collaboration.
2022. (Astronomy and Astrophysics).

Research output: Working paper

Harvard

Euclid Collaboration 2022 'Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models' Astronomy and Astrophysics. https://doi.org/10.1051/0004-6361/202141393

APA

Euclid Collaboration (2022). Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models. (Astronomy and Astrophysics). https://doi.org/10.1051/0004-6361/202141393

Vancouver

Euclid Collaboration. Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models. 2022 Jan 1. (Astronomy and Astrophysics). doi: 10.1051/0004-6361/202141393

Author

Euclid Collaboration. / Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models. 2022. (Astronomy and Astrophysics).

Bibtex

@techreport{df486ba99970458b90279bd0d1b7919b,
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.",
author = "{Euclid Collaboration} and Euclid Collaboration and H. Bretonni{\`e}re and M. Huertas-Company and A. Boucaud and F. Lanusse and E. Jullo and E. Merlin 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 V. Capobianco and C. Carbone and J. Carretero and S. Cavuoti and A. Cimatti and R. Cledassou and L. Corcione and A. Costille and H. Degaudenzi and M. Douspis and F. Dubath and S. Dusini and S. Ferriol and M. Frailis and E. Franceschi and M. Fumana and B. Garilli and C. Giocoli and A. Grazian and F. Grupp and Hook, {I. M.}",
year = "2022",
month = jan,
day = "1",
doi = "10.1051/0004-6361/202141393",
language = "English",
volume = "657",
series = "Astronomy and Astrophysics",
publisher = "EDP Sciences",
type = "WorkingPaper",
institution = "EDP Sciences",

}

RIS

TY - UNPB

T1 - Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

AU - Euclid Collaboration

AU - Collaboration, Euclid

AU - Bretonnière, H.

AU - Huertas-Company, M.

AU - Boucaud, A.

AU - Lanusse, F.

AU - Jullo, E.

AU - Merlin, E.

AU - Castellano, M.

AU - Brinchmann, J.

AU - Conselice, C. J.

AU - Dole, H.

AU - Cabanac, R.

AU - Courtois, H. M.

AU - Castander, F. J.

AU - Duc, P. A.

AU - Fosalba, P.

AU - Guinet, D.

AU - Kruk, S.

AU - Kuchner, U.

AU - Serrano, S.

AU - Soubrie, E.

AU - Tramacere, A.

AU - Wang, L.

AU - Amara, A.

AU - Auricchio, N.

AU - Bender, R.

AU - Bodendorf, C.

AU - Bonino, D.

AU - Branchini, E.

AU - Capobianco, V.

AU - Carbone, C.

AU - Carretero, J.

AU - Cavuoti, S.

AU - Cimatti, A.

AU - Cledassou, R.

AU - Corcione, L.

AU - Costille, A.

AU - Degaudenzi, H.

AU - Douspis, M.

AU - Dubath, F.

AU - Dusini, S.

AU - Ferriol, S.

AU - Frailis, M.

AU - Franceschi, E.

AU - Fumana, M.

AU - Garilli, B.

AU - Giocoli, C.

AU - Grazian, A.

AU - Grupp, F.

AU - Hook, I. M.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - 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 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.

AB - 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 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.

U2 - 10.1051/0004-6361/202141393

DO - 10.1051/0004-6361/202141393

M3 - Working paper

VL - 657

T3 - Astronomy and Astrophysics

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

ER -