Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning

Euclid Collaboration: I. Kovačić et al.

Detalles Bibliográficos
Autores: Euclid Collaboration, Kovačić, Inja, Siudek, Malgorzata, Castander, Francisco J., Fosalba, Pablo, Serrano, Santiago, Camacho-Quevedo, Benjamin, Gaztañaga, Enrique, Montoro, A., Courbin, Frédéric, Rebolo López, Rafael, Akrami, Yashar, García-Bellido, Juan
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/398388
Acceso en línea:http://hdl.handle.net/10261/398388
Access Level:acceso abierto
Palabra clave:Methods: statistical
Galaxies: general
Galaxies: photometry
id ES_707df4ac6cb7fedffd1be8cc3d41a8e5
oai_identifier_str oai:digital.csic.es:10261/398388
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
title Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
spellingShingle Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
Euclid Collaboration
Methods: statistical
Galaxies: general
Galaxies: photometry
title_short Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
title_full Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
title_fullStr Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
title_full_unstemmed Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
title_sort Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
dc.creator.none.fl_str_mv Euclid Collaboration
Kovačić, Inja
Siudek, Malgorzata
Castander, Francisco J.
Fosalba, Pablo
Serrano, Santiago
Camacho-Quevedo, Benjamin
Gaztañaga, Enrique
Montoro, A.
Courbin, Frédéric
Rebolo López, Rafael
Akrami, Yashar
García-Bellido, Juan
author Euclid Collaboration
author_facet Euclid Collaboration
Kovačić, Inja
Siudek, Malgorzata
Castander, Francisco J.
Fosalba, Pablo
Serrano, Santiago
Camacho-Quevedo, Benjamin
Gaztañaga, Enrique
Montoro, A.
Courbin, Frédéric
Rebolo López, Rafael
Akrami, Yashar
García-Bellido, Juan
author_role author
author2 Kovačić, Inja
Siudek, Malgorzata
Castander, Francisco J.
Fosalba, Pablo
Serrano, Santiago
Camacho-Quevedo, Benjamin
Gaztañaga, Enrique
Montoro, A.
Courbin, Frédéric
Rebolo López, Rafael
Akrami, Yashar
García-Bellido, Juan
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
European Space Agency
Kovačić, Inja [0000-0001-6751-3263]
Siudek, Malgorzata [0000-0002-2949-2155]
Castander, Francisco J. [0000-0001-7316-4573]
Fosalba, Pablo [0000-0002-1510-5214]
Serrano, Santiago [0000-0002-0211-2861]
Camacho-Quevedo, Benjamin [0000-0002-8789-4232]
Gaztañaga, Enrique [0000-0001-9632-0815]
Rebolo López, Rafael [0000-0003-3767-7085]
Akrami, Yashar [0000-0002-2407-7956]
García-Bellido, Juan [0000-0002-9370-8360]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Methods: statistical
Galaxies: general
Galaxies: photometry
topic Methods: statistical
Galaxies: general
Galaxies: photometry
description Euclid Collaboration: I. Kovačić et al.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/398388
url http://hdl.handle.net/10261/398388
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136505NB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1051/0004-6361/202453111
https://doi.org/10.1051/0004-6361/202453111
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learningEuclid CollaborationKovačić, InjaSiudek, MalgorzataCastander, Francisco J.Fosalba, PabloSerrano, SantiagoCamacho-Quevedo, BenjaminGaztañaga, EnriqueMontoro, A.Courbin, FrédéricRebolo López, RafaelAkrami, YasharGarcía-Bellido, JuanMethods: statisticalGalaxies: generalGalaxies: photometryEuclid Collaboration: I. Kovačić et al.The Euclid mission is generating a vast amount of imaging data in four broadband filters at a high angular resolution. This data will allow for the detailed study of mass, metallicity, and stellar populations across galaxies that will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. Here, we investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity, and age. We generated noise-free synthetic high-resolution (100 pc × 100 pc) imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images were generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We used a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a ≤0.130 dex scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but they still contain significant information that originates from underlying correlations at a sub-kiloparsec scales between stellar mass surface density and stellar population properties. As a corollary, we show that TNG50 follows a spatially resolved mass-metallicity relation that is consistent with observations. Due to its relatively low computational and time requirements, which has a time-frame of minutes without dedicated high performance computing infrastructure once it has been trained, our method allows for fast and robust estimates of the stellar mass surface density distributions of nearby galaxies from four-filter Euclid imaging data. Equivalent estimates of stellar population properties (stellar metallicity and age) are less robust but still hold value as first-order approximations across large samples.We thank the referee for useful comments that helped to improve the quality of the manuscript. Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. IK, MB and AN acknowledge support from the Belgian Science Policy Office (BELSPO) through the PRODEX project “Belgian Euclid Science Exploitation (BESE)” (No. 4000143202). JHK acknowledges grant PID2022-136505NB-I00 funded by MCIN/AEI/10.13039/501100011033 and EU, ERDF. We wish to thank the “Summer School for Astrostatistics in Crete” for providing training on the statistical methods adopted in this work. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft-und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Ciencia e Innovación, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org).Peer reviewedEuropean CommissionMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)European Space AgencyKovačić, Inja [0000-0001-6751-3263]Siudek, Malgorzata [0000-0002-2949-2155]Castander, Francisco J. [0000-0001-7316-4573]Fosalba, Pablo [0000-0002-1510-5214]Serrano, Santiago [0000-0002-0211-2861]Camacho-Quevedo, Benjamin [0000-0002-8789-4232]Gaztañaga, Enrique [0000-0001-9632-0815]Rebolo López, Rafael [0000-0003-3767-7085]Akrami, Yashar [0000-0002-2407-7956]García-Bellido, Juan [0000-0002-9370-8360]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/398388reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136505NB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1051/0004-6361/202453111https://doi.org/10.1051/0004-6361/202453111Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3983882026-05-22T06:33:51Z
score 15,81155

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