Astrometric constraints on stochastic gravitational wave background with neural networks

10 pages, 8 figures, comments welcome

Detalhes bibliográficos
Autores: Marienza Caldarola, Gonzalo Morrás, Santiago Jaraba, Sachiko Kuroyanagi, Savvas Nesseris, Juan García-Bellido
Tipo de documento: artigo
Estado:Versión enviada para evaluación y publicación
Data de publicação:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/390180
Acesso em linha:http://hdl.handle.net/10261/390180
http://arxiv.org/abs/2412.15879v1
Access Level:Acceso aberto
Palavra-chave:astro-ph.CO
General Relativity and Quantum Cosmology
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spelling Astrometric constraints on stochastic gravitational wave background with neural networksMarienza CaldarolaGonzalo MorrásSantiago JarabaSachiko KuroyanagiSavvas NesserisJuan García-Bellidoastro-ph.COastro-ph.COGeneral Relativity and Quantum Cosmology10 pages, 8 figures, comments welcomeAstrometric measurements provide a unique avenue for constraining the stochastic gravitational wave background (SGWB). In this work, we investigate the application of two neural network architectures, a fully connected network and a graph neural network, for analyzing astrometric data to detect the SGWB. Specifically, we generate mock Gaia astrometric measurements of the proper motions of sources and train two networks to predict the energy density of the SGWB, $\Omega_\text{GW}$. We evaluate the performance of both models under varying input datasets to assess their robustness across different configurations. Our results demonstrate that neural networks can effectively measure the SGWB, showing promise as tools for addressing systematic uncertainties and modeling limitations that pose challenges for traditional likelihood-based methods.They also acknowledge support from the research project PID2021-123012NB-C43 and the Spanish Research Agency (Agencia Estatal de Investigaci´on) through the Grant IFT Centro de Excelencia Severo Ochoa No CEX2020-001007-S, funded by MCIN/AEI/10.13039/501100011033. The authors also acknowledge the use of the IFT Hydra cluster. M.C. acknowledges support from the “Ram´on Areces” Foundation through the “Programa de Ayudas Fundaci´on Ram´on Areces para la realizaci´on de Tesis Doctorales en Ciencias de la Vida y de la Materia 2023”. G.M. acknowledges support from the Ministerio de Universidades through Grant No. FPU20/02857. S.J. acknowledges support from the Agence Nationale de la Recherche (ANR) under contract ANR-22-CE31-0001- 01. S.K. is supported by the Spanish Atracci´on de Talento contract no. 2019-T1/TIC-13177 granted by Comunidad de Madrid, the I+D grant PID2020-118159GAC42 funded by MCIN/AEI/10.13039/501100011033, the i-LINK 2021 grant LINKA20416 of CSIC, and Japan Society for the Promotion of Science (JSPS) KAKENHI Grant no. 20H01899, 20H05853, and 23H00110.Peer reviewedAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/390180http://arxiv.org/abs/2412.15879v1reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3901802026-05-22T06:33:51Z
dc.title.none.fl_str_mv Astrometric constraints on stochastic gravitational wave background with neural networks
title Astrometric constraints on stochastic gravitational wave background with neural networks
spellingShingle Astrometric constraints on stochastic gravitational wave background with neural networks
Marienza Caldarola
astro-ph.CO
astro-ph.CO
General Relativity and Quantum Cosmology
title_short Astrometric constraints on stochastic gravitational wave background with neural networks
title_full Astrometric constraints on stochastic gravitational wave background with neural networks
title_fullStr Astrometric constraints on stochastic gravitational wave background with neural networks
title_full_unstemmed Astrometric constraints on stochastic gravitational wave background with neural networks
title_sort Astrometric constraints on stochastic gravitational wave background with neural networks
dc.creator.none.fl_str_mv Marienza Caldarola
Gonzalo Morrás
Santiago Jaraba
Sachiko Kuroyanagi
Savvas Nesseris
Juan García-Bellido
author Marienza Caldarola
author_facet Marienza Caldarola
Gonzalo Morrás
Santiago Jaraba
Sachiko Kuroyanagi
Savvas Nesseris
Juan García-Bellido
author_role author
author2 Gonzalo Morrás
Santiago Jaraba
Sachiko Kuroyanagi
Savvas Nesseris
Juan García-Bellido
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv astro-ph.CO
astro-ph.CO
General Relativity and Quantum Cosmology
topic astro-ph.CO
astro-ph.CO
General Relativity and Quantum Cosmology
description 10 pages, 8 figures, comments welcome
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/390180
http://arxiv.org/abs/2412.15879v1
url http://hdl.handle.net/10261/390180
http://arxiv.org/abs/2412.15879v1
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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score 15,811543

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