Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

Artículo escrito por un elevado número de autores, sólo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiere

Detalles Bibliográficos
Autores: Bretonnière, H., García-Bellido Capdevila, Juan, Martinelli, Matteo, Euclid Collaboration
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/705901
Acceso en línea:http://hdl.handle.net/10486/705901
https://dx.doi.org/10.1051/0004-6361/202141393
Access Level:acceso abierto
Palabra clave:Cosmology: observations
Galaxies: evolution
Galaxies: structure
Surveys
Techniques: image processing
Física
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repository_id_str
spelling Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative modelsBretonnière, H.García-Bellido Capdevila, JuanMartinelli, MatteoEuclid CollaborationCosmology: observationsGalaxies: evolutionGalaxies: structureSurveysTechniques: image processingFísicaArtículo escrito por un elevado número de autores, sólo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiereWe 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 galaxiesEDP SciencesDepartamento de Física TeóricaFacultad de Ciencias20222022-01-18research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/705901https://dx.doi.org/10.1051/0004-6361/202141393reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7059012026-06-23T12:46:27Z
dc.title.none.fl_str_mv Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
title Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
spellingShingle Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
Bretonnière, H.
Cosmology: observations
Galaxies: evolution
Galaxies: structure
Surveys
Techniques: image processing
Física
title_short Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
title_full Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
title_fullStr Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
title_full_unstemmed Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
title_sort Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
dc.creator.none.fl_str_mv Bretonnière, H.
García-Bellido Capdevila, Juan
Martinelli, Matteo
Euclid Collaboration
author Bretonnière, H.
author_facet Bretonnière, H.
García-Bellido Capdevila, Juan
Martinelli, Matteo
Euclid Collaboration
author_role author
author2 García-Bellido Capdevila, Juan
Martinelli, Matteo
Euclid Collaboration
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Física Teórica
Facultad de Ciencias
dc.subject.none.fl_str_mv Cosmology: observations
Galaxies: evolution
Galaxies: structure
Surveys
Techniques: image processing
Física
topic Cosmology: observations
Galaxies: evolution
Galaxies: structure
Surveys
Techniques: image processing
Física
description Artículo escrito por un elevado número de autores, sólo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiere
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-18
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/705901
https://dx.doi.org/10.1051/0004-6361/202141393
url http://hdl.handle.net/10486/705901
https://dx.doi.org/10.1051/0004-6361/202141393
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
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:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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