The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc

Autores
Daza Perilla, Ingrid Vanessa; Sgró, Mario Agustín; Baravalle, Laura Daniela; Alonso, Maria Victoria; Villalón, Carolina Inés; Lares Harbin Latorre, Marcelo; Soto, M.; Nilo Castellón, José Luis; Valotto, Carlos Alberto; Cortés, P. Marchant; Minniti, Dante; Hempel, M.
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.
Fil: Daza Perilla, Ingrid Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Sgró, Mario Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Baravalle, Laura Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Alonso, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Villalón, Carolina Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Lares Harbin Latorre, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Soto, M.. Universidad de Atacama; Chile
Fil: Nilo Castellón, José Luis. Universidad de La Serena; Chile
Fil: Valotto, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Cortés, P. Marchant. Universidad de La Serena; Chile
Fil: Minniti, Dante. Universidad Andrés Bello; Chile. Universidade Federal de Santa Catarina; Brasil. Vatican Observatory; Italia
Fil: Hempel, M.. Universidad Andrés Bello; Chile. Max Planck Institute for Astronomy; Alemania
Materia
Methods: data analysis
Methods: statistical
Galaxy: general
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/248424
Acceder
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spelling The VVV near-IR galaxy catalogue in a Northern part of the Galactic discDaza Perilla, Ingrid VanessaSgró, Mario AgustínBaravalle, Laura DanielaAlonso, Maria VictoriaVillalón, Carolina InésLares Harbin Latorre, MarceloSoto, M.Nilo Castellón, José LuisValotto, Carlos AlbertoCortés, P. MarchantMinniti, DanteHempel, M.Methods: data analysisMethods: statisticalGalaxy: generalhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.Fil: Daza Perilla, Ingrid Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Sgró, Mario Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Baravalle, Laura Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Alonso, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Villalón, Carolina Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Lares Harbin Latorre, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Soto, M.. Universidad de Atacama; ChileFil: Nilo Castellón, José Luis. Universidad de La Serena; ChileFil: Valotto, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; ArgentinaFil: Cortés, P. Marchant. Universidad de La Serena; ChileFil: Minniti, Dante. Universidad Andrés Bello; Chile. Universidade Federal de Santa Catarina; Brasil. Vatican Observatory; ItaliaFil: Hempel, M.. Universidad Andrés Bello; Chile. Max Planck Institute for Astronomy; AlemaniaWiley Blackwell Publishing, Inc2023-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/248424Daza Perilla, Ingrid Vanessa; Sgró, Mario Agustín; Baravalle, Laura Daniela; Alonso, Maria Victoria; Villalón, Carolina Inés; et al.; The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 524; 1; 9-2023; 678-6940035-8711CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/mnras/article/524/1/678/7199801info:eu-repo/semantics/altIdentifier/doi/10.1093/mnras/stad1767info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2306.07141info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:30:17Zoai:ri.conicet.gov.ar:11336/248424instacron: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:34982025-09-29 10:30:17.379CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
title The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
spellingShingle The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
Daza Perilla, Ingrid Vanessa
Methods: data analysis
Methods: statistical
Galaxy: general
title_short The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
title_full The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
title_fullStr The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
title_full_unstemmed The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
title_sort The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc
dc.creator.none.fl_str_mv Daza Perilla, Ingrid Vanessa
Sgró, Mario Agustín
Baravalle, Laura Daniela
Alonso, Maria Victoria
Villalón, Carolina Inés
Lares Harbin Latorre, Marcelo
Soto, M.
Nilo Castellón, José Luis
Valotto, Carlos Alberto
Cortés, P. Marchant
Minniti, Dante
Hempel, M.
author Daza Perilla, Ingrid Vanessa
author_facet Daza Perilla, Ingrid Vanessa
Sgró, Mario Agustín
Baravalle, Laura Daniela
Alonso, Maria Victoria
Villalón, Carolina Inés
Lares Harbin Latorre, Marcelo
Soto, M.
Nilo Castellón, José Luis
Valotto, Carlos Alberto
Cortés, P. Marchant
Minniti, Dante
Hempel, M.
author_role author
author2 Sgró, Mario Agustín
Baravalle, Laura Daniela
Alonso, Maria Victoria
Villalón, Carolina Inés
Lares Harbin Latorre, Marcelo
Soto, M.
Nilo Castellón, José Luis
Valotto, Carlos Alberto
Cortés, P. Marchant
Minniti, Dante
Hempel, M.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Methods: data analysis
Methods: statistical
Galaxy: general
topic Methods: data analysis
Methods: statistical
Galaxy: general
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.
Fil: Daza Perilla, Ingrid Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Sgró, Mario Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Baravalle, Laura Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Alonso, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Villalón, Carolina Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Lares Harbin Latorre, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Soto, M.. Universidad de Atacama; Chile
Fil: Nilo Castellón, José Luis. Universidad de La Serena; Chile
Fil: Valotto, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina
Fil: Cortés, P. Marchant. Universidad de La Serena; Chile
Fil: Minniti, Dante. Universidad Andrés Bello; Chile. Universidade Federal de Santa Catarina; Brasil. Vatican Observatory; Italia
Fil: Hempel, M.. Universidad Andrés Bello; Chile. Max Planck Institute for Astronomy; Alemania
description The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more challenging due to the dust extinction. We present the methodology for the automatic classification of galaxies and non-galaxies at low Galactic latitude regions using both images and photometric and morphological near-IR data from the VISTA Variables in the Vía Láctea eXtended (VVVX) survey. Using the VVV NIR Galaxy Catalogue (VVV NIRGC), we analyse by statistical methods the most relevant features for galaxy identification. This catalogue was used to train a convolutional neural network with image data and an XGBoost model with both photometric and morphological data and then to generate a data set of extragalactic candidates. This allows us to derive probability catalogues used to analyse the completeness and purity as a function of the configuration parameters and to explore the best combinations of the models. As a test case, we apply this methodology to the Northern disc region of the VVVX survey, obtaining 172 396 extragalactic candidates with probabilities of being galaxies. We analyse the performance of our methodology in the VVV disc, reaching an F1-score of 0.67, a 65 per cent purity, and a 69 per cent completeness. We present the VVV NIRGC: Northern part of the Galactic disc comprising 1003 new galaxies, with probabilities greater than 0.6 for either model, with visual inspection and with only two previously identified galaxies. In the future, we intend to apply this methodology to other areas of the VVVX survey.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
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/11336/248424
Daza Perilla, Ingrid Vanessa; Sgró, Mario Agustín; Baravalle, Laura Daniela; Alonso, Maria Victoria; Villalón, Carolina Inés; et al.; The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 524; 1; 9-2023; 678-694
0035-8711
CONICET Digital
CONICET
url http://hdl.handle.net/11336/248424
identifier_str_mv Daza Perilla, Ingrid Vanessa; Sgró, Mario Agustín; Baravalle, Laura Daniela; Alonso, Maria Victoria; Villalón, Carolina Inés; et al.; The VVV near-IR galaxy catalogue in a Northern part of the Galactic disc; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 524; 1; 9-2023; 678-694
0035-8711
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/mnras/article/524/1/678/7199801
info:eu-repo/semantics/altIdentifier/doi/10.1093/mnras/stad1767
info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2306.07141
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
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
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
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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