Intelligent Supernovae Classification Systems in the KDUST context
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000200403 |
Resumo: | Abstract With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types. |
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Anais da Academia Brasileira de Ciências (Online) |
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Intelligent Supernovae Classification Systems in the KDUST contextDeep LearningArtificial IntelligenceSupernovaeType Ia SupernovaClassificationAbstract With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000200403Anais da Academia Brasileira de Ciências v.93 suppl.1 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120200862info:eu-repo/semantics/openAccessARANTES FILHO,LUÍS R.ROSA,REINALDO R.GUIMARÃES,LAMARTINE N.F.eng2021-02-19T00:00:00Zoai:scielo:S0001-37652021000200403Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-02-19T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Intelligent Supernovae Classification Systems in the KDUST context |
title |
Intelligent Supernovae Classification Systems in the KDUST context |
spellingShingle |
Intelligent Supernovae Classification Systems in the KDUST context ARANTES FILHO,LUÍS R. Deep Learning Artificial Intelligence Supernovae Type Ia Supernova Classification |
title_short |
Intelligent Supernovae Classification Systems in the KDUST context |
title_full |
Intelligent Supernovae Classification Systems in the KDUST context |
title_fullStr |
Intelligent Supernovae Classification Systems in the KDUST context |
title_full_unstemmed |
Intelligent Supernovae Classification Systems in the KDUST context |
title_sort |
Intelligent Supernovae Classification Systems in the KDUST context |
author |
ARANTES FILHO,LUÍS R. |
author_facet |
ARANTES FILHO,LUÍS R. ROSA,REINALDO R. GUIMARÃES,LAMARTINE N.F. |
author_role |
author |
author2 |
ROSA,REINALDO R. GUIMARÃES,LAMARTINE N.F. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
ARANTES FILHO,LUÍS R. ROSA,REINALDO R. GUIMARÃES,LAMARTINE N.F. |
dc.subject.por.fl_str_mv |
Deep Learning Artificial Intelligence Supernovae Type Ia Supernova Classification |
topic |
Deep Learning Artificial Intelligence Supernovae Type Ia Supernova Classification |
description |
Abstract With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000200403 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000200403 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120200862 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 suppl.1 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302870013870080 |