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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/246130
Ver los metadatos del registro completo
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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 Técnicas2025-10-22T12:07:12Zoai: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:34982025-10-22 12:07:12.71CONICET 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 |
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article |
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publishedVersion |
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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 |
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
| dc.publisher.none.fl_str_mv |
Wiley Blackwell Publishing, Inc |
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Wiley Blackwell Publishing, Inc |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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