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. 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 $0.4{\mathrm{deg}}^2$ 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 Sérsic 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 $22.5\mathrm{mag}{\mathrm{arcsec}}^{-2}$, and $24.9\mathrm{mag}{\mathrm{arcsec}}^{-2}$ 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 $10^{10.6}{\mathrm{M}}_{\odot }$ (resp. $10^{9.6}{\mathrm{M}}_{\odot }$) at a redshift $z\sim 0.5$ 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.

arXiv comment22 pages, 21 figures (6 in appendinx). Accepted for publication to A&A. Change in the title for Euclid submission chronology coherence. Few changes for clarification

References in corpus (29)

• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Detailed Structural Decomposition of Galaxy Images
• The Cosmic Evolution Survey (COSMOS) -- Overview
• The Great Observatories Origins Deep Survey: Initial Results From Optical and Near-Infrared Imaging
• 3D-HST+CANDELS: The Evolution of the Galaxy Size-Mass Distribution since $z=3$
• Normalizing Flows: An Introduction and Review of Current Methods
• The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies
• A Catalog of Bulge+Disk Decompositions and Updated Photometry for 1.12 Million Galaxies in the Sloan Digital Sky Survey
• The Evolution of Galaxy Structure over Cosmic Time
• The Evolution of Galaxy Mergers and Morphology at z<1.2 in the Extended Groth Strip
• Weak Gravitational Lensing with COSMOS: Galaxy Selection and Shape Measurements
• CANDELS: Constraining the AGN-Merger Connection with Host Morphologies at z~2
• The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
• MegaMorph - multi-wavelength measurement of galaxy structure: complete Sérsic profile information from modern surveys
• Lightcone mock catalogues from semi-analytic models of galaxy formation - I. Construction and application to the BzK colour selection
• GALAPAGOS: From Pixels to Parameters
• The evolution of the mass-size relation for early type galaxies from z~1 to the present: dependence on environment, mass-range and detailed morphology
• Quenching as a Contest between Galaxy Halos and their Central Black Holes
• Precision simulation of ground-based lensing data using observations from space
• Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data
• A catalog of polychromatic bulge-disk decompositions of ~ 17.600 galaxies in CANDELS
• The relationship between fine galaxy stellar morphology and star formation activity in cosmological simulations: a deep learning view
• Detecting outliers in astronomical images with deep generative networks
• Deep Generative Models for Galaxy Image Simulations
• Deblending galaxies with Variational Autoencoders: a joint multi-band, multi-instrument approach
• Structured Variational Inference for Simulating Populations of Radio Galaxies

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