A1 Journal article (refereed)
Euclid preparation : XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models (2022)

Euclid Collaboration. (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

JYU authors or editors

Publication details

All authors or editors: Euclid Collaboration

Journal or series: Astronomy and Astrophysics

ISSN: 0004-6361

eISSN: 1432-0746

Publication year: 2022

Volume: 657

Article number: A90

Publisher: EDP Sciences

Publication country: France

Publication language: English

DOI: https://doi.org/10.1051/0004-6361/202141393

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80054

Publication is parallel published: https://arxiv.org/abs/2105.12149

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 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.

Keywords: cosmology; astronomy; galaxies; galaxy clusters; machine learning

Free keywords: techniques: image processing; surveys; galaxies: structure; galaxies: evolution; cosmology: observations

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 2