Reddening-Free Q Indices to Identify Be Star Candidates

Autores
Aidelman, Yael Judith; Escudero, Carlos Gabriel; Ronchetti, Franco; Quiroga, Facundo Manuel; Lanzarini, Laura Cristina
Año de publicación
2020
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Astronomical databases currently provide high-volume spectroscopic and photometric data. While spectroscopic data is better suited to the analysis of many astronomical objects, photometric data is relatively easier to obtain due to shorter telescope usage time. Therefore, there is a growing need to use photometric information to automatically identify objects for further detailed studies, specially H α emission line stars such as Be stars. Photometric color-color diagrams (CCDs) are commonly used to identify this kind of objects. However, their identification in CCDs is further complicated by the reddening effect caused by both the circumstellar and interstellar gas. This effect prevents the generalization of candidate identification systems. Therefore, in this work we evaluate the use of neural networks to identify Be star candidates from a set of OB-type stars. The networks are trained using a labeled subset of the VPHAS+ and 2MASS databases, with filters u, g, r, Hα, i, J, H , and K. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free Q indices to enhance the generalization of the model to other databases and objects. To test the validity of the approach, we manually labeled a subset of the database, and use it to evaluate candidate identification models. We also labeled an independent dataset for cross dataset evaluation. We evaluate the recall of the models at a 99% precision level on both test sets. Our results show that the proposed features provide a significant improvement over the original filter magnitudes.
Trabajo publicado en Rucci E., Naiouf M., Chichizola F., De Giusti L. (eds). Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2020. Communications in Computer and Information Science, vol. 1291. Springer, Cham.
Instituto de Astrofísica de La Plata
Instituto de Investigación en Informática
Comisión de Investigaciones Científicas de la provincia de Buenos Aires
Materia
Ciencias Astronómicas
Ciencias Informáticas
Stellar Classification
OB-type stars
Be stars
VPHAS+
2MASS
IPHAS
SDSS
LAMOST
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/132374

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network_name_str SEDICI (UNLP)
spelling Reddening-Free Q Indices to Identify Be Star CandidatesAidelman, Yael JudithEscudero, Carlos GabrielRonchetti, FrancoQuiroga, Facundo ManuelLanzarini, Laura CristinaCiencias AstronómicasCiencias InformáticasStellar ClassificationOB-type starsBe starsVPHAS+2MASSIPHASSDSSLAMOSTAstronomical databases currently provide high-volume spectroscopic and photometric data. While spectroscopic data is better suited to the analysis of many astronomical objects, photometric data is relatively easier to obtain due to shorter telescope usage time. Therefore, there is a growing need to use photometric information to automatically identify objects for further detailed studies, specially H α emission line stars such as Be stars. Photometric color-color diagrams (CCDs) are commonly used to identify this kind of objects. However, their identification in CCDs is further complicated by the reddening effect caused by both the circumstellar and interstellar gas. This effect prevents the generalization of candidate identification systems. Therefore, in this work we evaluate the use of neural networks to identify Be star candidates from a set of OB-type stars. The networks are trained using a labeled subset of the VPHAS+ and 2MASS databases, with filters u, g, r, Hα, i, J, H , and K. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free Q indices to enhance the generalization of the model to other databases and objects. To test the validity of the approach, we manually labeled a subset of the database, and use it to evaluate candidate identification models. We also labeled an independent dataset for cross dataset evaluation. We evaluate the recall of the models at a 99% precision level on both test sets. Our results show that the proposed features provide a significant improvement over the original filter magnitudes.Trabajo publicado en Rucci E., Naiouf M., Chichizola F., De Giusti L. (eds). <i>Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2020</i>. Communications in Computer and Information Science, vol. 1291. Springer, Cham.Instituto de Astrofísica de La PlataInstituto de Investigación en InformáticaComisión de Investigaciones Científicas de la provincia de Buenos Aires2020info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf111-123http://sedici.unlp.edu.ar/handle/10915/132374spainfo:eu-repo/semantics/altIdentifier/isbn/978-3-030-61218-4info:eu-repo/semantics/altIdentifier/issn/1865-0929info:eu-repo/semantics/altIdentifier/issn/1865-0937info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-61218-4_8info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:13Zoai:sedici.unlp.edu.ar:10915/132374Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:13.93SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Reddening-Free Q Indices to Identify Be Star Candidates
title Reddening-Free Q Indices to Identify Be Star Candidates
spellingShingle Reddening-Free Q Indices to Identify Be Star Candidates
Aidelman, Yael Judith
Ciencias Astronómicas
Ciencias Informáticas
Stellar Classification
OB-type stars
Be stars
VPHAS+
2MASS
IPHAS
SDSS
LAMOST
title_short Reddening-Free Q Indices to Identify Be Star Candidates
title_full Reddening-Free Q Indices to Identify Be Star Candidates
title_fullStr Reddening-Free Q Indices to Identify Be Star Candidates
title_full_unstemmed Reddening-Free Q Indices to Identify Be Star Candidates
title_sort Reddening-Free Q Indices to Identify Be Star Candidates
dc.creator.none.fl_str_mv Aidelman, Yael Judith
Escudero, Carlos Gabriel
Ronchetti, Franco
Quiroga, Facundo Manuel
Lanzarini, Laura Cristina
author Aidelman, Yael Judith
author_facet Aidelman, Yael Judith
Escudero, Carlos Gabriel
Ronchetti, Franco
Quiroga, Facundo Manuel
Lanzarini, Laura Cristina
author_role author
author2 Escudero, Carlos Gabriel
Ronchetti, Franco
Quiroga, Facundo Manuel
Lanzarini, Laura Cristina
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Astronómicas
Ciencias Informáticas
Stellar Classification
OB-type stars
Be stars
VPHAS+
2MASS
IPHAS
SDSS
LAMOST
topic Ciencias Astronómicas
Ciencias Informáticas
Stellar Classification
OB-type stars
Be stars
VPHAS+
2MASS
IPHAS
SDSS
LAMOST
dc.description.none.fl_txt_mv Astronomical databases currently provide high-volume spectroscopic and photometric data. While spectroscopic data is better suited to the analysis of many astronomical objects, photometric data is relatively easier to obtain due to shorter telescope usage time. Therefore, there is a growing need to use photometric information to automatically identify objects for further detailed studies, specially H α emission line stars such as Be stars. Photometric color-color diagrams (CCDs) are commonly used to identify this kind of objects. However, their identification in CCDs is further complicated by the reddening effect caused by both the circumstellar and interstellar gas. This effect prevents the generalization of candidate identification systems. Therefore, in this work we evaluate the use of neural networks to identify Be star candidates from a set of OB-type stars. The networks are trained using a labeled subset of the VPHAS+ and 2MASS databases, with filters u, g, r, Hα, i, J, H , and K. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free Q indices to enhance the generalization of the model to other databases and objects. To test the validity of the approach, we manually labeled a subset of the database, and use it to evaluate candidate identification models. We also labeled an independent dataset for cross dataset evaluation. We evaluate the recall of the models at a 99% precision level on both test sets. Our results show that the proposed features provide a significant improvement over the original filter magnitudes.
Trabajo publicado en Rucci E., Naiouf M., Chichizola F., De Giusti L. (eds). <i>Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2020</i>. Communications in Computer and Information Science, vol. 1291. Springer, Cham.
Instituto de Astrofísica de La Plata
Instituto de Investigación en Informática
Comisión de Investigaciones Científicas de la provincia de Buenos Aires
description Astronomical databases currently provide high-volume spectroscopic and photometric data. While spectroscopic data is better suited to the analysis of many astronomical objects, photometric data is relatively easier to obtain due to shorter telescope usage time. Therefore, there is a growing need to use photometric information to automatically identify objects for further detailed studies, specially H α emission line stars such as Be stars. Photometric color-color diagrams (CCDs) are commonly used to identify this kind of objects. However, their identification in CCDs is further complicated by the reddening effect caused by both the circumstellar and interstellar gas. This effect prevents the generalization of candidate identification systems. Therefore, in this work we evaluate the use of neural networks to identify Be star candidates from a set of OB-type stars. The networks are trained using a labeled subset of the VPHAS+ and 2MASS databases, with filters u, g, r, Hα, i, J, H , and K. In order to avoid the reddening effect, we propose and evaluate the use of reddening-free Q indices to enhance the generalization of the model to other databases and objects. To test the validity of the approach, we manually labeled a subset of the database, and use it to evaluate candidate identification models. We also labeled an independent dataset for cross dataset evaluation. We evaluate the recall of the models at a 99% precision level on both test sets. Our results show that the proposed features provide a significant improvement over the original filter magnitudes.
publishDate 2020
dc.date.none.fl_str_mv 2020
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