Measuring the Intracluster Light Fraction with Machine Learning

The intracluster light (ICL) is an important tracer of a galaxy cluster’s history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required for the majority of measurement algorithms. Upcoming large i...

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Autores: Canepa, Louisa, Brough, Sarah, Lanusse, Francois, Montes, Mireia, Hatch, Nina
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:10261/392394
Acceso en línea:http://hdl.handle.net/10261/392394
https://api.elsevier.com/content/abstract/scopus_id/85218951159
Access Level:acceso abierto
Palabra clave:Galaxy clusters
Galactic and extragalactic astronomy
Convolutional neural networks
http://astrothesaurus.org/uat/584
http://astrothesaurus.org/uat/563
http://astrothesaurus.org/uat/1938
id ES_f33b9541b3441a02fdd232512da04420
oai_identifier_str oai:digital.csic.es:10261/392394
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Measuring the Intracluster Light Fraction with Machine Learning
title Measuring the Intracluster Light Fraction with Machine Learning
spellingShingle Measuring the Intracluster Light Fraction with Machine Learning
Canepa, Louisa
Galaxy clusters
Galactic and extragalactic astronomy
Convolutional neural networks
http://astrothesaurus.org/uat/584
http://astrothesaurus.org/uat/563
http://astrothesaurus.org/uat/1938
title_short Measuring the Intracluster Light Fraction with Machine Learning
title_full Measuring the Intracluster Light Fraction with Machine Learning
title_fullStr Measuring the Intracluster Light Fraction with Machine Learning
title_full_unstemmed Measuring the Intracluster Light Fraction with Machine Learning
title_sort Measuring the Intracluster Light Fraction with Machine Learning
dc.creator.none.fl_str_mv Canepa, Louisa
Brough, Sarah
Lanusse, Francois
Montes, Mireia
Hatch, Nina
author Canepa, Louisa
author_facet Canepa, Louisa
Brough, Sarah
Lanusse, Francois
Montes, Mireia
Hatch, Nina
author_role author
author2 Brough, Sarah
Lanusse, Francois
Montes, Mireia
Hatch, Nina
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Australian Research Council
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Astronomical Society of Australia
University of New South Wales (Australia)
Canepa, Louisa [0009-0002-5450-6683]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Galaxy clusters
Galactic and extragalactic astronomy
Convolutional neural networks
http://astrothesaurus.org/uat/584
http://astrothesaurus.org/uat/563
http://astrothesaurus.org/uat/1938
topic Galaxy clusters
Galactic and extragalactic astronomy
Convolutional neural networks
http://astrothesaurus.org/uat/584
http://astrothesaurus.org/uat/563
http://astrothesaurus.org/uat/1938
description The intracluster light (ICL) is an important tracer of a galaxy cluster’s history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required for the majority of measurement algorithms. Upcoming large imaging surveys such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) are expected to vastly expand available samples of deep cluster images. However, to process this increased amount of data, we need faster, fully automated methods to streamline the measurement process. This paper presents a machine learning model designed to automatically measure the ICL fraction in large samples of images, with no manual preprocessing required. We train the fully supervised model on a training data set of 50,000 images with injected artificial ICL profiles. We then transfer its learning onto real data by fine-tuning with a sample of 101 real clusters with their ICL fraction measured manually using the surface brightness threshold method. With this process, the model is able to effectively learn the task and then adapt its learning to real cluster images. Our model can be directly applied to Hyper Suprime-Cam images, processing up to 500 images in a matter of seconds on a single GPU, or fine-tuned for other imaging surveys such as LSST, with the fine-tuning process taking just 3 minutes. The model could also be retrained to match other ICL measurement methods. Our model and the code for training it are made available on GitHub.
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/10261/392394
https://api.elsevier.com/content/abstract/scopus_id/85218951159
url http://hdl.handle.net/10261/392394
https://api.elsevier.com/content/abstract/scopus_id/85218951159
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//RYC2022-036949-I
https://doi.org/10.3847/1538-4357/adabc7
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 IOP Publishing
publisher.none.fl_str_mv IOP Publishing
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
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spelling Measuring the Intracluster Light Fraction with Machine LearningCanepa, LouisaBrough, SarahLanusse, FrancoisMontes, MireiaHatch, NinaGalaxy clustersGalactic and extragalactic astronomyConvolutional neural networkshttp://astrothesaurus.org/uat/584http://astrothesaurus.org/uat/563http://astrothesaurus.org/uat/1938The intracluster light (ICL) is an important tracer of a galaxy cluster’s history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required for the majority of measurement algorithms. Upcoming large imaging surveys such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) are expected to vastly expand available samples of deep cluster images. However, to process this increased amount of data, we need faster, fully automated methods to streamline the measurement process. This paper presents a machine learning model designed to automatically measure the ICL fraction in large samples of images, with no manual preprocessing required. We train the fully supervised model on a training data set of 50,000 images with injected artificial ICL profiles. We then transfer its learning onto real data by fine-tuning with a sample of 101 real clusters with their ICL fraction measured manually using the surface brightness threshold method. With this process, the model is able to effectively learn the task and then adapt its learning to real cluster images. Our model can be directly applied to Hyper Suprime-Cam images, processing up to 500 images in a matter of seconds on a single GPU, or fine-tuned for other imaging surveys such as LSST, with the fine-tuning process taking just 3 minutes. The model could also be retrained to match other ICL measurement methods. Our model and the code for training it are made available on GitHub.S.B. acknowledges funding support from the Australian Research Council through a Discovery Project DP190101943. M.M. acknowledges support from the project RYC2022-036949-I financed by the MICIU/AEI/10.13039/501100011033 and by FSE+. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project No. CE170100013. Parts of this research were supported by the Astronomical Society of Australia (ASA), through the Student Travel Assistance Scheme (STAS). This research includes computations using the computational cluster Katana supported by Research Technology Services at UNSW Sydney. The HSC collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from the Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. This paper makes use of software developed for Vera C. Rubin Observatory. We thank the Rubin Observatory for making their code available as free software at http://pipelines.lsst.io/.Peer reviewedIOP PublishingAustralian Research CouncilMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Astronomical Society of AustraliaUniversity of New South Wales (Australia)Canepa, Louisa [0009-0002-5450-6683]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/10261/392394https://api.elsevier.com/content/abstract/scopus_id/85218951159reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI//RYC2022-036949-Ihttps://doi.org/10.3847/1538-4357/adabc7Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3923942026-05-22T06:33:51Z
score 15,811543

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