From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning

Autores
Cabral, Juan Bautista; Sánchez, Bruno Orlando; Ramos Almendares, Felipe Alberto; Gurovich, Sebastian; Granitto, Pablo Miguel; Vanderplas, J.
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools.
Fil: Cabral, Juan Bautista. Universidad Nacional de Rosario; Argentina. 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: Sánchez, Bruno Orlando. 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: Ramos Almendares, Felipe 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: Gurovich, Sebastian. 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: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Vanderplas, J.. University of Washington; Estados Unidos
Materia
ASTROINFORMATICS
FEATURE SELECTION
MACHINE LEARNING ALGORITHM
SOFTWARE AND ITS ENGINEERING
SOFTWARE POST-DEVELOPMENT ISSUE
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:11336/87048
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/87048
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learningCabral, Juan BautistaSánchez, Bruno OrlandoRamos Almendares, Felipe AlbertoGurovich, SebastianGranitto, Pablo MiguelVanderplas, J.ASTROINFORMATICSFEATURE SELECTIONMACHINE LEARNING ALGORITHMSOFTWARE AND ITS ENGINEERINGSOFTWARE POST-DEVELOPMENT ISSUEhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools.Fil: Cabral, Juan Bautista. Universidad Nacional de Rosario; Argentina. 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: Sánchez, Bruno Orlando. 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: Ramos Almendares, Felipe 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: Gurovich, Sebastian. 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: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Vanderplas, J.. University of Washington; Estados UnidosElsevier2018-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87048Cabral, Juan Bautista; Sánchez, Bruno Orlando; Ramos Almendares, Felipe Alberto; Gurovich, Sebastian; Granitto, Pablo Miguel; et al.; From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning; Elsevier; Astronomy and Computing; 25; 10-2018; 213-2202213-1337CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2213133718300581info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ascom.2018.09.005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:18:51Zoai:ri.conicet.gov.ar:11336/87048instacron: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 10:18:51.468CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
title From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
spellingShingle From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
Cabral, Juan Bautista
ASTROINFORMATICS
FEATURE SELECTION
MACHINE LEARNING ALGORITHM
SOFTWARE AND ITS ENGINEERING
SOFTWARE POST-DEVELOPMENT ISSUE
title_short From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
title_full From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
title_fullStr From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
title_full_unstemmed From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
title_sort From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
dc.creator.none.fl_str_mv Cabral, Juan Bautista
Sánchez, Bruno Orlando
Ramos Almendares, Felipe Alberto
Gurovich, Sebastian
Granitto, Pablo Miguel
Vanderplas, J.
author Cabral, Juan Bautista
author_facet Cabral, Juan Bautista
Sánchez, Bruno Orlando
Ramos Almendares, Felipe Alberto
Gurovich, Sebastian
Granitto, Pablo Miguel
Vanderplas, J.
author_role author
author2 Sánchez, Bruno Orlando
Ramos Almendares, Felipe Alberto
Gurovich, Sebastian
Granitto, Pablo Miguel
Vanderplas, J.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv ASTROINFORMATICS
FEATURE SELECTION
MACHINE LEARNING ALGORITHM
SOFTWARE AND ITS ENGINEERING
SOFTWARE POST-DEVELOPMENT ISSUE
topic ASTROINFORMATICS
FEATURE SELECTION
MACHINE LEARNING ALGORITHM
SOFTWARE AND ITS ENGINEERING
SOFTWARE POST-DEVELOPMENT ISSUE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools.
Fil: Cabral, Juan Bautista. Universidad Nacional de Rosario; Argentina. 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: Sánchez, Bruno Orlando. 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: Ramos Almendares, Felipe 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: Gurovich, Sebastian. 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: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Vanderplas, J.. University of Washington; Estados Unidos
description Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools.
publishDate 2018
dc.date.none.fl_str_mv 2018-10
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/11336/87048
Cabral, Juan Bautista; Sánchez, Bruno Orlando; Ramos Almendares, Felipe Alberto; Gurovich, Sebastian; Granitto, Pablo Miguel; et al.; From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning; Elsevier; Astronomy and Computing; 25; 10-2018; 213-220
2213-1337
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87048
identifier_str_mv Cabral, Juan Bautista; Sánchez, Bruno Orlando; Ramos Almendares, Felipe Alberto; Gurovich, Sebastian; Granitto, Pablo Miguel; et al.; From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning; Elsevier; Astronomy and Computing; 25; 10-2018; 213-220
2213-1337
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://www.sciencedirect.com/science/article/pii/S2213133718300581
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ascom.2018.09.005
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 Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1844614154209460224
score 13.070432

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