Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data

Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel class...

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Autores: Viscasillas Vázquez, Carlos, Solano, Enrique, Ulla Viguesa, Ana, Ambrosch, M., Álvarez, M. A., Manteiga Outeiro, Minia, Magrini, L., Santoveña Gómez, Raúl, Dafonte, C., Pérez-Fernández, E., Aller, A., Drazdauskas, Arnas, Mikolaitis, Šarūnas, Rodrigo, C.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/385228
Acceso en línea:http://hdl.handle.net/10261/385228
http://arxiv.org/abs/2409.17783v1
Access Level:acceso abierto
Palabra clave:Binaries: general
Methods: data analysis
Subdwarfs
Techniques: spectroscopic
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oai_identifier_str oai:digital.csic.es:10261/385228
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
title Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
spellingShingle Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
Viscasillas Vázquez, Carlos
Binaries: general
Methods: data analysis
Subdwarfs
Techniques: spectroscopic
title_short Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
title_full Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
title_fullStr Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
title_full_unstemmed Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
title_sort Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 data
dc.creator.none.fl_str_mv Viscasillas Vázquez, Carlos
Solano, Enrique
Ulla Viguesa, Ana
Ambrosch, M.
Álvarez, M. A.
Manteiga Outeiro, Minia
Magrini, L.
Santoveña Gómez, Raúl
Dafonte, C.
Pérez-Fernández, E.
Aller, A.
Drazdauskas, Arnas
Mikolaitis, Šarūnas
Rodrigo, C.
author Viscasillas Vázquez, Carlos
author_facet Viscasillas Vázquez, Carlos
Solano, Enrique
Ulla Viguesa, Ana
Ambrosch, M.
Álvarez, M. A.
Manteiga Outeiro, Minia
Magrini, L.
Santoveña Gómez, Raúl
Dafonte, C.
Pérez-Fernández, E.
Aller, A.
Drazdauskas, Arnas
Mikolaitis, Šarūnas
Rodrigo, C.
author_role author
author2 Solano, Enrique
Ulla Viguesa, Ana
Ambrosch, M.
Álvarez, M. A.
Manteiga Outeiro, Minia
Magrini, L.
Santoveña Gómez, Raúl
Dafonte, C.
Pérez-Fernández, E.
Aller, A.
Drazdauskas, Arnas
Mikolaitis, Šarūnas
Rodrigo, C.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
NASA
European Commission
Viscasillas Vázquez, Carlos [0000-0001-5415-2796]
Solano, Enrique [0000-0003-1885-5130]
Ulla Viguesa, Ana [0000-0001-6424-5005]
Ambrosch, M. [0000-0003-0804-6938]
Álvarez, M. A. [0000-0002-6786-2620]
Manteiga Outeiro, Minia [0000-0002-7711-5581]
Magrini, L. [0000-0003-4486-6802]
Santoveña Gómez, Raúl [0000-0002-9257-2131]
Dafonte, C. [0000-0003-4693-7555]
Pérez-Fernández, E. [0000-0003-2335-1332]
A. Aller [0000-0003-0884-9589]
Drazdauskas, Arnas [0000-0001-5145-254X]
Mikolaitis, Šarūnas [0000-0002-1419-0517]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Binaries: general
Methods: data analysis
Subdwarfs
Techniques: spectroscopic
topic Binaries: general
Methods: data analysis
Subdwarfs
Techniques: spectroscopic
description Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence methods and Gaia DR3 data. The results will be compared with those obtained previously using VOSA (Virtual Observatory Sed Analyzer) on coincident samples. The methods include several machine learning techniques. We used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their color-magnitude properties. Of these, 2815 objects have Gaia Data Release 3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). The findings demonstrate a high agreement level (70-90%) with VOSA's classification, indicating that machine learning methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry. CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single-binary differences are especially observable on the infrared flux in our GDR3 BP/BR spectra, at wavelengths larger than 700 nm. We found that all our methods are effective in discerning between single and binary systems and are consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods are also effective for detecting peculiarities in the spectra. Further research is needed to refine our techniques and enhance automated classification reliability, especially for large-scale surveys.
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
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/385228
http://arxiv.org/abs/2409.17783v1
url http://hdl.handle.net/10261/385228
http://arxiv.org/abs/2409.17783v1
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/HE/HORIZON-CL4-2023-SPACE-01-71
A&A 691, A223 (2024)
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Astronomical Society
publisher.none.fl_str_mv American Astronomical Society
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
_version_ 1869420010902913024
spelling Advanced Classification of Hot Subdwarf Binaries Using Artificial Intelligence Techniques and Gaia DR3 dataViscasillas Vázquez, CarlosSolano, EnriqueUlla Viguesa, AnaAmbrosch, M.Álvarez, M. A.Manteiga Outeiro, MiniaMagrini, L.Santoveña Gómez, RaúlDafonte, C.Pérez-Fernández, E.Aller, A.Drazdauskas, ArnasMikolaitis, ŠarūnasRodrigo, C.Binaries: generalMethods: data analysisSubdwarfsTechniques: spectroscopicHot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence methods and Gaia DR3 data. The results will be compared with those obtained previously using VOSA (Virtual Observatory Sed Analyzer) on coincident samples. The methods include several machine learning techniques. We used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their color-magnitude properties. Of these, 2815 objects have Gaia Data Release 3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). The findings demonstrate a high agreement level (70-90%) with VOSA's classification, indicating that machine learning methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry. CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single-binary differences are especially observable on the infrared flux in our GDR3 BP/BR spectra, at wavelengths larger than 700 nm. We found that all our methods are effective in discerning between single and binary systems and are consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods are also effective for detecting peculiarities in the spectra. Further research is needed to refine our techniques and enhance automated classification reliability, especially for large-scale surveys.We sincerely thank the anonymous referee for her/his valuable guidelines and insightful comments, which have significantly enhanced the quality of this work. This research has made use of the Spanish Virtual Observatory (https://svo.cab.inta-csic.es) project funded by MCIN/AEI/10.13039/501100011033/ through grant PID2020-112949GB-I00. Also made use of GUASOM (Fustes et al. 2014; Álvarez et al. 2022), Scikit-learn Machine Learning (Pedregosa et al. 2011), NetworkX (Hagberg et al. 2008), Seaborn (Waskom 2021), TopCat (Taylor 2005), Pandas (The pandas development team 2020) and Matplotlib (Hunter 2007). This research has made extensive use of NASA’s Astrophysics Data System Bibliographic Services. This work has made use of data from the European Space Agency (ESA) Gaia mission, processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. This research has made use of the Simbad database and the Aladin sky atlas, operated at CDS, Strasbourg, France. The authors have also made use of the VOSA software, developed under the Spanish Virtual Observatory project supported by the Spanish MINECO through grant PID2020-112949GB-I00. Funding from Spanish Ministry project PID2021-122842OB-C22, Xunta de Galicia ED431B 2021/36 and PDC2021-121059-C22 is acknowledged by the authors. This work was funded by the Spanish MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR through grant PID2021-122842OB-C22 and the Horizon Europe [HORIZON-CL4-2023-SPACE-01-71], SPACIOUS project funded under Grant Agreement no. 101135205. CVV and AU thank the MW-Gaia COST Action “Revealing the MilkyWay with Gaia” CA18104 for its support through a Shortterm scientific mission (STSM) at the University of Vigo and to Erasmus+Sta for supporting a scientific visit of CVV to the aforementioned university. MAA, MM, RSG and JCD also acknowledge support from CIGUS CITIC, funded by Xunta de Galicia and the European Union (FEDER Galicia 2021-2027 Program) through grant ED431G 2023/01. This work is in the memory of Carlos Rodrigo (y), deceased during the preparation of this work.Peer reviewedAmerican Astronomical SocietyMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)NASAEuropean CommissionViscasillas Vázquez, Carlos [0000-0001-5415-2796]Solano, Enrique [0000-0003-1885-5130]Ulla Viguesa, Ana [0000-0001-6424-5005]Ambrosch, M. [0000-0003-0804-6938]Álvarez, M. A. [0000-0002-6786-2620]Manteiga Outeiro, Minia [0000-0002-7711-5581]Magrini, L. [0000-0003-4486-6802]Santoveña Gómez, Raúl [0000-0002-9257-2131]Dafonte, C. [0000-0003-4693-7555]Pérez-Fernández, E. [0000-0003-2335-1332]A. Aller [0000-0003-0884-9589]Drazdauskas, Arnas [0000-0001-5145-254X]Mikolaitis, Šarūnas [0000-0002-1419-0517]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/385228http://arxiv.org/abs/2409.17783v1reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/HORIZON-CL4-2023-SPACE-01-71A&A 691, A223 (2024)Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3852282026-05-22T06:33:51Z
score 15,811543

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