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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 ; Hook, Isobel.
In: Astronomy and Astrophysics, Vol. 657, A90, 31.01.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Euclid Collaboration & Hook, I 2022, 'Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models', Astronomy and Astrophysics, vol. 657, A90. https://doi.org/10.1051/0004-6361/202141393

APA

Euclid Collaboration, & Hook, I. (2022). Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models. Astronomy and Astrophysics, 657, Article A90. https://doi.org/10.1051/0004-6361/202141393

Vancouver

Euclid Collaboration, Hook I. Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models. Astronomy and Astrophysics. 2022 Jan 31;657:A90. Epub 2022 Jan 18. doi: 10.1051/0004-6361/202141393

Author

Euclid Collaboration ; Hook, Isobel. / Euclid preparation : XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models. In: Astronomy and Astrophysics. 2022 ; Vol. 657.

Bibtex

@article{93334055edaf4c2d86bf6e9fc74518d9,
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 Isobel Hook",
year = "2022",
month = jan,
day = "31",
doi = "10.1051/0004-6361/202141393",
language = "English",
volume = "657",
journal = "Astronomy and Astrophysics",
issn = "1432-0746",
publisher = "EDP Sciences",

}

RIS

TY - JOUR

T1 - Euclid preparation

T2 - XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

AU - Euclid Collaboration

AU - Hook, Isobel

PY - 2022/1/31

Y1 - 2022/1/31

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 - Journal article

VL - 657

JO - Astronomy and Astrophysics

JF - Astronomy and Astrophysics

SN - 1432-0746

M1 - A90

ER -