An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3

Autores
Bom, C. R.; Cortesi, A.; Ribeiro, U.; Dias, L. O.; Kelkar, K.; Smith Castelli, Analia Viviana; Santana Silva, L.; Lopes Silva, V.; Gonçalves, T. S.; Abramo, L. R.; Lima, E. V. R.; Almeida Fernandes, F.; Espinosa, L.; Li, L.; Buzzo, M.L.; Mendes de Oliveira, Claudia Lucia; Sodré, Laerte; Ferrari, F.; Alvarez Candal, A.; Grossi, M.; Telles, E.; Torres Flores, S.; Werner, S. V.; Kanaan, A.; Ribeiro, T.; Schoenell, W.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to rpetro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues.
Fil: Bom, C. R.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Cortesi, A.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Ribeiro, U.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Dias, L. O.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Kelkar, K.. Universidad Técnica Federico Santa María; Chile
Fil: Smith Castelli, Analia Viviana. Universidad Nacional de La Plata; Argentina. 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: Santana Silva, L.. Universidade Cruzeiro Do Sul; Brasil
Fil: Lopes Silva, V.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Gonçalves, T. S.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Abramo, L. R.. Universidade de Sao Paulo; Brasil
Fil: Lima, E. V. R.. Universidade de Sao Paulo; Brasil
Fil: Almeida Fernandes, F.. Universidade de Sao Paulo; Brasil
Fil: Espinosa, L.. Universidade de Sao Paulo; Brasil
Fil: Li, L.. Universidade de Sao Paulo; Brasil
Fil: Buzzo, M.L.. Swinburne University Of Technology; Australia
Fil: Mendes de Oliveira, Claudia Lucia. Universidade de Sao Paulo; Brasil
Fil: Sodré, Laerte. Universidade de Sao Paulo; Brasil
Fil: Ferrari, F.. Universidade Federal do Rio Grande; Brasil
Fil: Alvarez Candal, A.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Grossi, M.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Torres Flores, S.. Universidad de La Serena; Chile
Fil: Werner, S. V.. Science and Technology Facilities Council of Nottingham. Rutherford Appleton Laboratory; Reino Unido. University of Nottingham; Estados Unidos
Fil: Kanaan, A.. Universidade Federal de Santa Catarina; Brasil
Fil: Ribeiro, T.. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Schoenell, W.. National Optical Astronomy Observatory; Estados Unidos
Materia
CATALOGUES
GALAXIES: FUNDAMENTAL PARAMETERS
GALAXIES: STRUCTURE
TECHNIQUES: IMAGE PROCESSING
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:11336/246130

id CONICETDig_808445904ec6af352e0636f96f2c7bb6
oai_identifier_str oai:ri.conicet.gov.ar:11336/246130
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3Bom, C. R.Cortesi, A.Ribeiro, U.Dias, L. O.Kelkar, K.Smith Castelli, Analia VivianaSantana Silva, L.Lopes Silva, V.Gonçalves, T. S.Abramo, L. R.Lima, E. V. R.Almeida Fernandes, F.Espinosa, L.Li, L.Buzzo, M.L.Mendes de Oliveira, Claudia LuciaSodré, LaerteFerrari, F.Alvarez Candal, A.Grossi, M.Telles, E.Torres Flores, S.Werner, S. V.Kanaan, A.Ribeiro, T.Schoenell, W.CATALOGUESGALAXIES: FUNDAMENTAL PARAMETERSGALAXIES: STRUCTURETECHNIQUES: IMAGE PROCESSINGhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to rpetro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues.Fil: Bom, C. R.. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Cortesi, A.. Universidade Federal do Rio de Janeiro; BrasilFil: Ribeiro, U.. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Dias, L. O.. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Kelkar, K.. Universidad Técnica Federico Santa María; ChileFil: Smith Castelli, Analia Viviana. Universidad Nacional de La Plata; Argentina. 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: Santana Silva, L.. Universidade Cruzeiro Do Sul; BrasilFil: Lopes Silva, V.. Universidade Federal do Rio de Janeiro; BrasilFil: Gonçalves, T. S.. Universidade Federal do Rio de Janeiro; BrasilFil: Abramo, L. R.. Universidade de Sao Paulo; BrasilFil: Lima, E. V. R.. Universidade de Sao Paulo; BrasilFil: Almeida Fernandes, F.. Universidade de Sao Paulo; BrasilFil: Espinosa, L.. Universidade de Sao Paulo; BrasilFil: Li, L.. Universidade de Sao Paulo; BrasilFil: Buzzo, M.L.. Swinburne University Of Technology; AustraliaFil: Mendes de Oliveira, Claudia Lucia. Universidade de Sao Paulo; BrasilFil: Sodré, Laerte. Universidade de Sao Paulo; BrasilFil: Ferrari, F.. Universidade Federal do Rio Grande; BrasilFil: Alvarez Candal, A.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Grossi, M.. Universidade Federal do Rio de Janeiro; BrasilFil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; BrasilFil: Torres Flores, S.. Universidad de La Serena; ChileFil: Werner, S. V.. Science and Technology Facilities Council of Nottingham. Rutherford Appleton Laboratory; Reino Unido. University of Nottingham; Estados UnidosFil: Kanaan, A.. Universidade Federal de Santa Catarina; BrasilFil: Ribeiro, T.. Universidade Federal do Rio Grande do Sul; BrasilFil: Schoenell, W.. National Optical Astronomy Observatory; Estados UnidosWiley Blackwell Publishing, Inc2024-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/246130Bom, C. R.; Cortesi, A.; Ribeiro, U.; Dias, L. O.; Kelkar, K.; et al.; An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 528; 3; 3-2024; 4188-42080035-8711CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1093/mnras/stad3956info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/mnras/article/528/3/4188/7492270info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:34:47Zoai:ri.conicet.gov.ar:11336/246130instacron: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 10:34:47.631CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
title An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
spellingShingle An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
Bom, C. R.
CATALOGUES
GALAXIES: FUNDAMENTAL PARAMETERS
GALAXIES: STRUCTURE
TECHNIQUES: IMAGE PROCESSING
title_short An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
title_full An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
title_fullStr An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
title_full_unstemmed An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
title_sort An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3
dc.creator.none.fl_str_mv Bom, C. R.
Cortesi, A.
Ribeiro, U.
Dias, L. O.
Kelkar, K.
Smith Castelli, Analia Viviana
Santana Silva, L.
Lopes Silva, V.
Gonçalves, T. S.
Abramo, L. R.
Lima, E. V. R.
Almeida Fernandes, F.
Espinosa, L.
Li, L.
Buzzo, M.L.
Mendes de Oliveira, Claudia Lucia
Sodré, Laerte
Ferrari, F.
Alvarez Candal, A.
Grossi, M.
Telles, E.
Torres Flores, S.
Werner, S. V.
Kanaan, A.
Ribeiro, T.
Schoenell, W.
author Bom, C. R.
author_facet Bom, C. R.
Cortesi, A.
Ribeiro, U.
Dias, L. O.
Kelkar, K.
Smith Castelli, Analia Viviana
Santana Silva, L.
Lopes Silva, V.
Gonçalves, T. S.
Abramo, L. R.
Lima, E. V. R.
Almeida Fernandes, F.
Espinosa, L.
Li, L.
Buzzo, M.L.
Mendes de Oliveira, Claudia Lucia
Sodré, Laerte
Ferrari, F.
Alvarez Candal, A.
Grossi, M.
Telles, E.
Torres Flores, S.
Werner, S. V.
Kanaan, A.
Ribeiro, T.
Schoenell, W.
author_role author
author2 Cortesi, A.
Ribeiro, U.
Dias, L. O.
Kelkar, K.
Smith Castelli, Analia Viviana
Santana Silva, L.
Lopes Silva, V.
Gonçalves, T. S.
Abramo, L. R.
Lima, E. V. R.
Almeida Fernandes, F.
Espinosa, L.
Li, L.
Buzzo, M.L.
Mendes de Oliveira, Claudia Lucia
Sodré, Laerte
Ferrari, F.
Alvarez Candal, A.
Grossi, M.
Telles, E.
Torres Flores, S.
Werner, S. V.
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
author
author
author
author
author
dc.subject.none.fl_str_mv CATALOGUES
GALAXIES: FUNDAMENTAL PARAMETERS
GALAXIES: STRUCTURE
TECHNIQUES: IMAGE PROCESSING
topic CATALOGUES
GALAXIES: FUNDAMENTAL PARAMETERS
GALAXIES: STRUCTURE
TECHNIQUES: IMAGE PROCESSING
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 morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to rpetro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues.
Fil: Bom, C. R.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Cortesi, A.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Ribeiro, U.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Dias, L. O.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Kelkar, K.. Universidad Técnica Federico Santa María; Chile
Fil: Smith Castelli, Analia Viviana. Universidad Nacional de La Plata; Argentina. 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: Santana Silva, L.. Universidade Cruzeiro Do Sul; Brasil
Fil: Lopes Silva, V.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Gonçalves, T. S.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Abramo, L. R.. Universidade de Sao Paulo; Brasil
Fil: Lima, E. V. R.. Universidade de Sao Paulo; Brasil
Fil: Almeida Fernandes, F.. Universidade de Sao Paulo; Brasil
Fil: Espinosa, L.. Universidade de Sao Paulo; Brasil
Fil: Li, L.. Universidade de Sao Paulo; Brasil
Fil: Buzzo, M.L.. Swinburne University Of Technology; Australia
Fil: Mendes de Oliveira, Claudia Lucia. Universidade de Sao Paulo; Brasil
Fil: Sodré, Laerte. Universidade de Sao Paulo; Brasil
Fil: Ferrari, F.. Universidade Federal do Rio Grande; Brasil
Fil: Alvarez Candal, A.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Grossi, M.. Universidade Federal do Rio de Janeiro; Brasil
Fil: Telles, E.. Ministério de Ciencia, Tecnologia e Innovacao. Observatorio Nacional; Brasil
Fil: Torres Flores, S.. Universidad de La Serena; Chile
Fil: Werner, S. V.. Science and Technology Facilities Council of Nottingham. Rutherford Appleton Laboratory; Reino Unido. University of Nottingham; Estados Unidos
Fil: Kanaan, A.. Universidade Federal de Santa Catarina; Brasil
Fil: Ribeiro, T.. Universidade Federal do Rio Grande do Sul; Brasil
Fil: Schoenell, W.. National Optical Astronomy Observatory; Estados Unidos
description The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to rpetro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues.
publishDate 2024
dc.date.none.fl_str_mv 2024-03
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/11336/246130
Bom, C. R.; Cortesi, A.; Ribeiro, U.; Dias, L. O.; Kelkar, K.; et al.; An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 528; 3; 3-2024; 4188-4208
0035-8711
CONICET Digital
CONICET
url http://hdl.handle.net/11336/246130
identifier_str_mv Bom, C. R.; Cortesi, A.; Ribeiro, U.; Dias, L. O.; Kelkar, K.; et al.; An extended catalogue of galaxy morphology using deep learning in southern photometric local universe survey data release 3; Wiley Blackwell Publishing, Inc; Monthly Notices of the Royal Astronomical Society; 528; 3; 3-2024; 4188-4208
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/doi/10.1093/mnras/stad3956
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/mnras/article/528/3/4188/7492270
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
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
_version_ 1844614365126328320
score 13.070432