Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data

Autores: Gutierrez Soto, Luis Angel; Lopes de Oliveira, R.; Akras, S.; Goncalves Gama, Diana Renata; Lomelí Núñez, L. F.; Mendes de Oliveira, Claudia Lucia; Telles, E.; Alvarez Candal, A.; Borges Fernandes, M.; Daflon, S.; Ferreira Lopes, C. E.; Grossi, M.; Hazarika, D.; Humire, P. K.; Lima Dias, C.; Reis Lopes, Amanda; Nilo Castellón, José Luis; Panda, S.; Kanaan, A.; Ribeiro, T.; Schoenell, W.
Año de publicación: 2025
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify Hα excess point source candidates in the southern sky. This approach combines photometric data from 12 S-PLUS filters with machine learning techniques to improve source classification and advance our understanding of Hα-related phenomena. Aims. Our goal is to enhance the classification of Hα excess point sources by distinguishing between Galactic and extragalactic objects, particularly those with redshifted emission lines, and to identify sources where the Hα excess is associated with variability phenomena, such as short-period RR Lyrae stars. Methods. We selected Hα excess candidates using the (r − J0660) versus (r − i) colour–colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). For the MS sample, dimensionality reduction was achieved using UMAP, followed by HDBSCAN clustering. We refined this by incorporating infrared data, which improved the separation of source types. A random forest model was then trained on the clustering results to identify key colour features for the classification of Hα excess sources. New effective colour–colour diagrams were constructed by combining data from S-PLUS MS and infrared data. These diagrams, alongside tentative colour criteria, offer a preliminary classification of Hα excess sources without the need for complex algorithms. Results. Combining multi-wavelength photometric data with machine learning techniques significantly improved the classification of Hα excess sources. We identified 6956 sources with an excess in the J0660 filter, and cross-matching with SIMBAD allowed us to explore the types of objects present in our catalogue, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, variable stars, and extragalactic sources such as Quasi-Stellar Objects (QSOs), Active Galactic Nuclei (AGN), and galaxies. The cross-match also revealed X-ray sources, transients, and other peculiar objects. Using S-PLUS colours and machine learning, we successfully separated RR Lyrae stars from other Galactic stars and from extragalactic objects. Additionally, we achieved a clear separation between Galactic and extragalactic sources. However, distinguishing cataclysmic variables from QSOs at specific redshifts remained challenging. Incorporating infrared data refined the classification, enabling us to separate Galactic from extragalactic sources and to distinguish cataclysmic variables from QSOs. The Random Forest model, trained on HDBSCAN results, highlighted key colour features that distinguish the different classes of Hα excess sources, providing a robust framework for future studies, such as follow-up spectroscopy.
Fil: Gutierrez Soto, Luis Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina
Fil: Lopes de Oliveira, R.. Universidade Federal de Sergipe; Brasil. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Akras, S.. National Observatory of Athens; Grecia
Fil: Goncalves Gama, Diana Renata. Universidade Federal do Rio de Janeiro; Brasil
Fil: Lomelí Núñez, L. F.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Mendes de Oliveira, Claudia Lucia. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; Brasil
Fil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Alvarez Candal, A.. Instituto de Astrofísica de Andalucía; España. Consejo Superior de Investigaciones Científicas; España
Fil: Borges Fernandes, M.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Daflon, S.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Ferreira Lopes, C. E.. Universidad de Atacama.; Chile
Fil: Grossi, M.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Hazarika, D.. Universidad de Atacama.; Chile
Fil: Humire, P. K.. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; Brasil
Fil: Lima Dias, C.. Universidad de La Serena; Chile
Fil: Reis Lopes, Amanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina
Fil: Nilo Castellón, José Luis. Universidad de La Serena; Chile
Fil: Panda, S.. International Gemini Observatory; Brasil
Fil: Kanaan, A.. Universidade Federal de Santa Catarina; Brasil
Fil: Ribeiro, T.. Rubin Observatory Project Office; Estados Unidos
Fil: Schoenell, W.. The Observatories Of The Carnegie Institution For Scien; Estados Unidos
Materia: surveys
echniques: photometric
stars: novae, cataclysmic variables
quasars: emission lines
Nivel de accesibilidad: acceso abierto
Condiciones de uso: https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
Institución: Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador: oai:ri.conicet.gov.ar:11336/291748

Acceder

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network_acronym_str	CONICETDig
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network_name_str	CONICET Digital (CONICET)
spelling	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS dataGutierrez Soto, Luis AngelLopes de Oliveira, R.Akras, S.Goncalves Gama, Diana RenataLomelí Núñez, L. F.Mendes de Oliveira, Claudia LuciaTelles, E.Alvarez Candal, A.Borges Fernandes, M.Daflon, S.Ferreira Lopes, C. E.Grossi, M.Hazarika, D.Humire, P. K.Lima Dias, C.Reis Lopes, AmandaNilo Castellón, José LuisPanda, S.Kanaan, A.Ribeiro, T.Schoenell, W.surveysechniques: photometricstars: novae, cataclysmic variablesquasars: emission lineshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify Hα excess point source candidates in the southern sky. This approach combines photometric data from 12 S-PLUS filters with machine learning techniques to improve source classification and advance our understanding of Hα-related phenomena. Aims. Our goal is to enhance the classification of Hα excess point sources by distinguishing between Galactic and extragalactic objects, particularly those with redshifted emission lines, and to identify sources where the Hα excess is associated with variability phenomena, such as short-period RR Lyrae stars. Methods. We selected Hα excess candidates using the (r − J0660) versus (r − i) colour–colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). For the MS sample, dimensionality reduction was achieved using UMAP, followed by HDBSCAN clustering. We refined this by incorporating infrared data, which improved the separation of source types. A random forest model was then trained on the clustering results to identify key colour features for the classification of Hα excess sources. New effective colour–colour diagrams were constructed by combining data from S-PLUS MS and infrared data. These diagrams, alongside tentative colour criteria, offer a preliminary classification of Hα excess sources without the need for complex algorithms. Results. Combining multi-wavelength photometric data with machine learning techniques significantly improved the classification of Hα excess sources. We identified 6956 sources with an excess in the J0660 filter, and cross-matching with SIMBAD allowed us to explore the types of objects present in our catalogue, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, variable stars, and extragalactic sources such as Quasi-Stellar Objects (QSOs), Active Galactic Nuclei (AGN), and galaxies. The cross-match also revealed X-ray sources, transients, and other peculiar objects. Using S-PLUS colours and machine learning, we successfully separated RR Lyrae stars from other Galactic stars and from extragalactic objects. Additionally, we achieved a clear separation between Galactic and extragalactic sources. However, distinguishing cataclysmic variables from QSOs at specific redshifts remained challenging. Incorporating infrared data refined the classification, enabling us to separate Galactic from extragalactic sources and to distinguish cataclysmic variables from QSOs. The Random Forest model, trained on HDBSCAN results, highlighted key colour features that distinguish the different classes of Hα excess sources, providing a robust framework for future studies, such as follow-up spectroscopy.Fil: Gutierrez Soto, Luis Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Lopes de Oliveira, R.. Universidade Federal de Sergipe; Brasil. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Akras, S.. National Observatory of Athens; GreciaFil: Goncalves Gama, Diana Renata. Universidade Federal do Rio de Janeiro; BrasilFil: Lomelí Núñez, L. F.. Universidade Federal do Rio de Janeiro; BrasilFil: Mendes de Oliveira, Claudia Lucia. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; BrasilFil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Alvarez Candal, A.. Instituto de Astrofísica de Andalucía; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Borges Fernandes, M.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Daflon, S.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Ferreira Lopes, C. E.. Universidad de Atacama.; ChileFil: Grossi, M.. Universidade Federal do Rio de Janeiro; BrasilFil: Hazarika, D.. Universidad de Atacama.; ChileFil: Humire, P. K.. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; BrasilFil: Lima Dias, C.. Universidad de La Serena; ChileFil: Reis Lopes, Amanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Nilo Castellón, José Luis. Universidad de La Serena; ChileFil: Panda, S.. International Gemini Observatory; BrasilFil: Kanaan, A.. Universidade Federal de Santa Catarina; BrasilFil: Ribeiro, T.. Rubin Observatory Project Office; Estados UnidosFil: Schoenell, W.. The Observatories Of The Carnegie Institution For Scien; Estados UnidosEDP Sciences2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/291748Gutierrez Soto, Luis Angel; Lopes de Oliveira, R.; Akras, S.; Goncalves Gama, Diana Renata; Lomelí Núñez, L. F.; et al.; Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data; EDP Sciences; Astronomy and Astrophysics; 695; A104; 2-2025; 1-230004-6361CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.aanda.org/10.1051/0004-6361/202453167info:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/202453167info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T15:31:46Zoai:ri.conicet.gov.ar:11336/291748instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 15:31:47.115CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
title	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
spellingShingle	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data Gutierrez Soto, Luis Angel surveys echniques: photometric stars: novae, cataclysmic variables quasars: emission lines
title_short	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
title_full	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
title_fullStr	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
title_full_unstemmed	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
title_sort	Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data
dc.creator.none.fl_str_mv	Gutierrez Soto, Luis Angel Lopes de Oliveira, R. Akras, S. Goncalves Gama, Diana Renata Lomelí Núñez, L. F. Mendes de Oliveira, Claudia Lucia Telles, E. Alvarez Candal, A. Borges Fernandes, M. Daflon, S. Ferreira Lopes, C. E. Grossi, M. Hazarika, D. Humire, P. K. Lima Dias, C. Reis Lopes, Amanda Nilo Castellón, José Luis Panda, S. Kanaan, A. Ribeiro, T. Schoenell, W.
author	Gutierrez Soto, Luis Angel
author_facet	Gutierrez Soto, Luis Angel Lopes de Oliveira, R. Akras, S. Goncalves Gama, Diana Renata Lomelí Núñez, L. F. Mendes de Oliveira, Claudia Lucia Telles, E. Alvarez Candal, A. Borges Fernandes, M. Daflon, S. Ferreira Lopes, C. E. Grossi, M. Hazarika, D. Humire, P. K. Lima Dias, C. Reis Lopes, Amanda Nilo Castellón, José Luis Panda, S. Kanaan, A. Ribeiro, T. Schoenell, W.
author_role	author
author2	Lopes de Oliveira, R. Akras, S. Goncalves Gama, Diana Renata Lomelí Núñez, L. F. Mendes de Oliveira, Claudia Lucia Telles, E. Alvarez Candal, A. Borges Fernandes, M. Daflon, S. Ferreira Lopes, C. E. Grossi, M. Hazarika, D. Humire, P. K. Lima Dias, C. Reis Lopes, Amanda Nilo Castellón, José Luis Panda, S. Kanaan, A. Ribeiro, T. Schoenell, W.
author2_role	author author author author author author author author author author author author author author author author author author author author
dc.subject.none.fl_str_mv	surveys echniques: photometric stars: novae, cataclysmic variables quasars: emission lines
topic	surveys echniques: photometric stars: novae, cataclysmic variables quasars: emission lines
purl_subject.fl_str_mv	https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv	Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify Hα excess point source candidates in the southern sky. This approach combines photometric data from 12 S-PLUS filters with machine learning techniques to improve source classification and advance our understanding of Hα-related phenomena. Aims. Our goal is to enhance the classification of Hα excess point sources by distinguishing between Galactic and extragalactic objects, particularly those with redshifted emission lines, and to identify sources where the Hα excess is associated with variability phenomena, such as short-period RR Lyrae stars. Methods. We selected Hα excess candidates using the (r − J0660) versus (r − i) colour–colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). For the MS sample, dimensionality reduction was achieved using UMAP, followed by HDBSCAN clustering. We refined this by incorporating infrared data, which improved the separation of source types. A random forest model was then trained on the clustering results to identify key colour features for the classification of Hα excess sources. New effective colour–colour diagrams were constructed by combining data from S-PLUS MS and infrared data. These diagrams, alongside tentative colour criteria, offer a preliminary classification of Hα excess sources without the need for complex algorithms. Results. Combining multi-wavelength photometric data with machine learning techniques significantly improved the classification of Hα excess sources. We identified 6956 sources with an excess in the J0660 filter, and cross-matching with SIMBAD allowed us to explore the types of objects present in our catalogue, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, variable stars, and extragalactic sources such as Quasi-Stellar Objects (QSOs), Active Galactic Nuclei (AGN), and galaxies. The cross-match also revealed X-ray sources, transients, and other peculiar objects. Using S-PLUS colours and machine learning, we successfully separated RR Lyrae stars from other Galactic stars and from extragalactic objects. Additionally, we achieved a clear separation between Galactic and extragalactic sources. However, distinguishing cataclysmic variables from QSOs at specific redshifts remained challenging. Incorporating infrared data refined the classification, enabling us to separate Galactic from extragalactic sources and to distinguish cataclysmic variables from QSOs. The Random Forest model, trained on HDBSCAN results, highlighted key colour features that distinguish the different classes of Hα excess sources, providing a robust framework for future studies, such as follow-up spectroscopy. Fil: Gutierrez Soto, Luis Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina Fil: Lopes de Oliveira, R.. Universidade Federal de Sergipe; Brasil. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil Fil: Akras, S.. National Observatory of Athens; Grecia Fil: Goncalves Gama, Diana Renata. Universidade Federal do Rio de Janeiro; Brasil Fil: Lomelí Núñez, L. F.. Universidade Federal do Rio de Janeiro; Brasil Fil: Mendes de Oliveira, Claudia Lucia. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; Brasil Fil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil Fil: Alvarez Candal, A.. Instituto de Astrofísica de Andalucía; España. Consejo Superior de Investigaciones Científicas; España Fil: Borges Fernandes, M.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil Fil: Daflon, S.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil Fil: Ferreira Lopes, C. E.. Universidad de Atacama.; Chile Fil: Grossi, M.. Universidade Federal do Rio de Janeiro; Brasil Fil: Hazarika, D.. Universidad de Atacama.; Chile Fil: Humire, P. K.. Universidade Do Sao Paulo. Instituto Astronomia, Geofísica E Ciencias Atmosfericas. Departamento de Astronomia; Brasil Fil: Lima Dias, C.. Universidad de La Serena; Chile Fil: Reis Lopes, Amanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina Fil: Nilo Castellón, José Luis. Universidad de La Serena; Chile Fil: Panda, S.. International Gemini Observatory; Brasil Fil: Kanaan, A.. Universidade Federal de Santa Catarina; Brasil Fil: Ribeiro, T.. Rubin Observatory Project Office; Estados Unidos Fil: Schoenell, W.. The Observatories Of The Carnegie Institution For Scien; Estados Unidos
description	Context. We use the Southern Photometric Local Universe Survey (S-PLUS) Fourth Data Release (DR4) to identify and classify Hα excess point source candidates in the southern sky. This approach combines photometric data from 12 S-PLUS filters with machine learning techniques to improve source classification and advance our understanding of Hα-related phenomena. Aims. Our goal is to enhance the classification of Hα excess point sources by distinguishing between Galactic and extragalactic objects, particularly those with redshifted emission lines, and to identify sources where the Hα excess is associated with variability phenomena, such as short-period RR Lyrae stars. Methods. We selected Hα excess candidates using the (r − J0660) versus (r − i) colour–colour diagram from the S-PLUS main survey (MS) and Galactic Disk Survey (GDS). For the MS sample, dimensionality reduction was achieved using UMAP, followed by HDBSCAN clustering. We refined this by incorporating infrared data, which improved the separation of source types. A random forest model was then trained on the clustering results to identify key colour features for the classification of Hα excess sources. New effective colour–colour diagrams were constructed by combining data from S-PLUS MS and infrared data. These diagrams, alongside tentative colour criteria, offer a preliminary classification of Hα excess sources without the need for complex algorithms. Results. Combining multi-wavelength photometric data with machine learning techniques significantly improved the classification of Hα excess sources. We identified 6956 sources with an excess in the J0660 filter, and cross-matching with SIMBAD allowed us to explore the types of objects present in our catalogue, including emission-line stars, young stellar objects, nebulae, stellar binaries, cataclysmic variables, variable stars, and extragalactic sources such as Quasi-Stellar Objects (QSOs), Active Galactic Nuclei (AGN), and galaxies. The cross-match also revealed X-ray sources, transients, and other peculiar objects. Using S-PLUS colours and machine learning, we successfully separated RR Lyrae stars from other Galactic stars and from extragalactic objects. Additionally, we achieved a clear separation between Galactic and extragalactic sources. However, distinguishing cataclysmic variables from QSOs at specific redshifts remained challenging. Incorporating infrared data refined the classification, enabling us to separate Galactic from extragalactic sources and to distinguish cataclysmic variables from QSOs. The Random Forest model, trained on HDBSCAN results, highlighted key colour features that distinguish the different classes of Hα excess sources, providing a robust framework for future studies, such as follow-up spectroscopy.
publishDate	2025
dc.date.none.fl_str_mv	2025-02
dc.type.none.fl_str_mv	info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo
format	article
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/11336/291748 Gutierrez Soto, Luis Angel; Lopes de Oliveira, R.; Akras, S.; Goncalves Gama, Diana Renata; Lomelí Núñez, L. F.; et al.; Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data; EDP Sciences; Astronomy and Astrophysics; 695; A104; 2-2025; 1-23 0004-6361 CONICET Digital CONICET
url	http://hdl.handle.net/11336/291748
identifier_str_mv	Gutierrez Soto, Luis Angel; Lopes de Oliveira, R.; Akras, S.; Goncalves Gama, Diana Renata; Lomelí Núñez, L. F.; et al.; Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data; EDP Sciences; Astronomy and Astrophysics; 695; A104; 2-2025; 1-23 0004-6361 CONICET Digital CONICET
dc.language.none.fl_str_mv	eng
language	eng
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dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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rights_invalid_str_mv	https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv	application/pdf application/pdf application/pdf
dc.publisher.none.fl_str_mv	EDP Sciences
publisher.none.fl_str_mv	EDP Sciences
dc.source.none.fl_str_mv	reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas
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repository.name.fl_str_mv	CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv	dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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Mapping Hα excess candidate point sources in the southern hemisphere using S-PLUS data

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