Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples

Context. Previous attempts to separate Small Magellanic Cloud (SMC) stars from the Milky Way (MW) foreground stars are based only on the proper motions of the stars. Aims. In this paper, we aim to develop a statistical classification technique to effectively separate the SMC stars from the MW stars...

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Authors: Jiménez Arranz, Óscar, Romero Gómez, Mercè, Luri Carrascoso, Xavier, Masana, Eulàlia
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/215264
Online Access:http://hdl.handle.net/2445/215264
Access Level:Open access
Keyword:Astrometria
Núvols de Magalhães
Astrometry
Magellanic Clouds
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spelling Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samplesJiménez Arranz, ÓscarRomero Gómez, MercèLuri Carrascoso, XavierMasana, EulàliaAstrometriaNúvols de MagalhãesAstrometryMagellanic CloudsContext. Previous attempts to separate Small Magellanic Cloud (SMC) stars from the Milky Way (MW) foreground stars are based only on the proper motions of the stars. Aims. In this paper, we aim to develop a statistical classification technique to effectively separate the SMC stars from the MW stars using a wider set of Gaia data. We aim to reduce the possible contamination from MW stars compared to previous strategies. Methods. The new strategy is based on a neural network classifier, applied to the bulk of the Gaia DR3 data. We produce three samples of stars flagged as SMC members, with varying levels of completeness and purity, obtained by application of this classifier. Using different test samples, we validated these classification results and compared them with the results of the selection technique employed in the Gaia Collaboration papers, which was based solely on the proper motions. Results. The contamination of the MW in each of the three SMC samples is estimated to be in the 10–40% range; the “best case” in this range is obtained for bright stars (G < 16), which belong to the Vlos sub-samples, and the “worst case” for the full SMC sample determined by using very stringent criteria based on StarHorse distances. A further check based on the comparison with a nearby area with uniform sky density indicates that the global contamination in our samples is probably close to the low end of the range, around 10%. Conclusions. We provide three selections of SMC star samples with different degrees of purity and completeness, for which we estimate a low contamination level and which we have successfully validated using SMC RR Lyrae, SMC Cepheids, and SMC-MW StarHorse samples.EDP Sciences2024202420232024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion1 p.application/pdfhttp://hdl.handle.net/2445/215264http://hdl.handle.net/2445/215264reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1051/0004-6361/202245720Astronomy & Astrophysics, 2023https://doi.org/10.1051/0004-6361/202245720(c) The European Southern Observatory (ESO), 2023info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2152642026-05-29T05:05:01Z
dc.title.none.fl_str_mv Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
title Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
spellingShingle Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
Jiménez Arranz, Óscar
Astrometria
Núvols de Magalhães
Astrometry
Magellanic Clouds
title_short Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
title_full Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
title_fullStr Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
title_full_unstemmed Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
title_sort Application of a Neural Network classifier for the generation of clean Small Magellanic Cloud stellar samples
dc.creator.none.fl_str_mv Jiménez Arranz, Óscar
Romero Gómez, Mercè
Luri Carrascoso, Xavier
Masana, Eulàlia
author Jiménez Arranz, Óscar
author_facet Jiménez Arranz, Óscar
Romero Gómez, Mercè
Luri Carrascoso, Xavier
Masana, Eulàlia
author_role author
author2 Romero Gómez, Mercè
Luri Carrascoso, Xavier
Masana, Eulàlia
author2_role author
author
author
dc.subject.none.fl_str_mv Astrometria
Núvols de Magalhães
Astrometry
Magellanic Clouds
topic Astrometria
Núvols de Magalhães
Astrometry
Magellanic Clouds
description Context. Previous attempts to separate Small Magellanic Cloud (SMC) stars from the Milky Way (MW) foreground stars are based only on the proper motions of the stars. Aims. In this paper, we aim to develop a statistical classification technique to effectively separate the SMC stars from the MW stars using a wider set of Gaia data. We aim to reduce the possible contamination from MW stars compared to previous strategies. Methods. The new strategy is based on a neural network classifier, applied to the bulk of the Gaia DR3 data. We produce three samples of stars flagged as SMC members, with varying levels of completeness and purity, obtained by application of this classifier. Using different test samples, we validated these classification results and compared them with the results of the selection technique employed in the Gaia Collaboration papers, which was based solely on the proper motions. Results. The contamination of the MW in each of the three SMC samples is estimated to be in the 10–40% range; the “best case” in this range is obtained for bright stars (G < 16), which belong to the Vlos sub-samples, and the “worst case” for the full SMC sample determined by using very stringent criteria based on StarHorse distances. A further check based on the comparison with a nearby area with uniform sky density indicates that the global contamination in our samples is probably close to the low end of the range, around 10%. Conclusions. We provide three selections of SMC star samples with different degrees of purity and completeness, for which we estimate a low contamination level and which we have successfully validated using SMC RR Lyrae, SMC Cepheids, and SMC-MW StarHorse samples.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2445/215264
http://hdl.handle.net/2445/215264
url http://hdl.handle.net/2445/215264
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1051/0004-6361/202245720
Astronomy & Astrophysics, 2023
https://doi.org/10.1051/0004-6361/202245720
dc.rights.none.fl_str_mv (c) The European Southern Observatory (ESO), 2023
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) The European Southern Observatory (ESO), 2023
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1 p.
application/pdf
dc.publisher.none.fl_str_mv EDP Sciences
publisher.none.fl_str_mv EDP Sciences
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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