Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

Euclid Collaboration: et al.

Detalles Bibliográficos
Autores: Euclid Collaboration, Castander, Francisco J., García-Bellido, Juan, Martinelli, Matteo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/337624
Acceso en línea:http://hdl.handle.net/10261/337624
Access Level:acceso abierto
Palabra clave:Galaxies: photometry
Galaxies: high-redshift
Galaxies: evolution
Galaxies: general
Methods: statistical
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spelling Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learningEuclid CollaborationCastander, Francisco J.García-Bellido, JuanMartinelli, MatteoGalaxies: photometryGalaxies: high-redshiftGalaxies: evolutionGalaxies: generalMethods: statisticalEuclid Collaboration: et al.The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE – YE, JE – HE and u – IE, IE – JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2.This work was supported by Fundação para a Ciência e a Tecnologia (FCT) through grants UID/FIS/04434/2019, UIDB/04434/2020, UIDP/04434/2020 and PTDC/FIS-AST/29245/2017, and an FCT-CAPES Transnational Cooperation Project. LB acknowledges financial support by the Agenzia Spaziale Italiana (ASI) under the research contract 2018-31-HH.0. KIC acknowledges funding from the European Research Council through the award of the Consolidator Grant ID 681627-BUILDUP. AH acknowledges support from the NVIDIA Academic Hardware Grant Program.Peer reviewedEDP SciencesFundação para a Ciência e a Tecnologia (Portugal)European Research CouncilAgenzia Spaziale ItalianaNVIDIA CorporationConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/337624reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/681627https://doi.org/10.1051/0004-6361/202244307Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3376242026-05-22T06:33:51Z
dc.title.none.fl_str_mv Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
title Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
spellingShingle Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
Euclid Collaboration
Galaxies: photometry
Galaxies: high-redshift
Galaxies: evolution
Galaxies: general
Methods: statistical
title_short Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
title_full Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
title_fullStr Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
title_full_unstemmed Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
title_sort Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning
dc.creator.none.fl_str_mv Euclid Collaboration
Castander, Francisco J.
García-Bellido, Juan
Martinelli, Matteo
author Euclid Collaboration
author_facet Euclid Collaboration
Castander, Francisco J.
García-Bellido, Juan
Martinelli, Matteo
author_role author
author2 Castander, Francisco J.
García-Bellido, Juan
Martinelli, Matteo
author2_role author
author
author
dc.contributor.none.fl_str_mv Fundação para a Ciência e a Tecnologia (Portugal)
European Research Council
Agenzia Spaziale Italiana
NVIDIA Corporation
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Galaxies: photometry
Galaxies: high-redshift
Galaxies: evolution
Galaxies: general
Methods: statistical
topic Galaxies: photometry
Galaxies: high-redshift
Galaxies: evolution
Galaxies: general
Methods: statistical
description Euclid Collaboration: et al.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
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/10261/337624
url http://hdl.handle.net/10261/337624
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/EC/H2020/681627
https://doi.org/10.1051/0004-6361/202244307
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.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
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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