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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/132374
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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. |
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2020 |
dc.date.none.fl_str_mv |
2020 |
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