ASteCA: Automated Stellar Cluster Analysis

Autores
Perren, Gabriel Ignacio; Vazquez, Ruben Angel; Piatti, Andres Eduardo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster’s center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster’s metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.
Fil: Perren, Gabriel Ignacio. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; Argentina
Fil: Vazquez, Ruben Angel. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; Argentina
Fil: Piatti, Andres Eduardo. Universidad Nacional de Cordoba. Observatorio Astronomico de Cordoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Methods: Statistical
Galaxies: Star Clusters: General
Open Clusters And Associations: General
Techniques: Photometric
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:11336/15112
Acceder
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spelling ASteCA: Automated Stellar Cluster AnalysisPerren, Gabriel IgnacioVazquez, Ruben AngelPiatti, Andres EduardoMethods: StatisticalGalaxies: Star Clusters: GeneralOpen Clusters And Associations: GeneralTechniques: Photometrichttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster’s center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster’s metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.Fil: Perren, Gabriel Ignacio. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; ArgentinaFil: Vazquez, Ruben Angel. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; ArgentinaFil: Piatti, Andres Eduardo. Universidad Nacional de Cordoba. Observatorio Astronomico de Cordoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaEdp Sciences2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15112Perren, Gabriel Ignacio; Vazquez, Ruben Angel; Piatti, Andres Eduardo; ASteCA: Automated Stellar Cluster Analysis; Edp Sciences; Astronomy And Astrophysics; 576; 2015; 1-29; A60004-63611432-0746enginfo:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/201424946info:eu-repo/semantics/altIdentifier/url/http://www.aanda.org/articles/aa/abs/2015/04/aa24946-14/aa24946-14.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:41:29Zoai:ri.conicet.gov.ar:11336/15112instacron: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:34982025-10-15 15:41:29.741CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv ASteCA: Automated Stellar Cluster Analysis
title ASteCA: Automated Stellar Cluster Analysis
spellingShingle ASteCA: Automated Stellar Cluster Analysis
Perren, Gabriel Ignacio
Methods: Statistical
Galaxies: Star Clusters: General
Open Clusters And Associations: General
Techniques: Photometric
title_short ASteCA: Automated Stellar Cluster Analysis
title_full ASteCA: Automated Stellar Cluster Analysis
title_fullStr ASteCA: Automated Stellar Cluster Analysis
title_full_unstemmed ASteCA: Automated Stellar Cluster Analysis
title_sort ASteCA: Automated Stellar Cluster Analysis
dc.creator.none.fl_str_mv Perren, Gabriel Ignacio
Vazquez, Ruben Angel
Piatti, Andres Eduardo
author Perren, Gabriel Ignacio
author_facet Perren, Gabriel Ignacio
Vazquez, Ruben Angel
Piatti, Andres Eduardo
author_role author
author2 Vazquez, Ruben Angel
Piatti, Andres Eduardo
author2_role author
author
dc.subject.none.fl_str_mv Methods: Statistical
Galaxies: Star Clusters: General
Open Clusters And Associations: General
Techniques: Photometric
topic Methods: Statistical
Galaxies: Star Clusters: General
Open Clusters And Associations: General
Techniques: Photometric
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster’s center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster’s metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.
Fil: Perren, Gabriel Ignacio. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; Argentina
Fil: Vazquez, Ruben Angel. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Instituto de Astrofísica de La Plata; Argentina; Argentina
Fil: Piatti, Andres Eduardo. Universidad Nacional de Cordoba. Observatorio Astronomico de Cordoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We present the Automated Stellar Cluster Analysis package (ASteCA), a suit of tools designed to fully automate the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster’s center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster’s metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from its official site.
publishDate 2015
dc.date.none.fl_str_mv 2015
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/11336/15112
Perren, Gabriel Ignacio; Vazquez, Ruben Angel; Piatti, Andres Eduardo; ASteCA: Automated Stellar Cluster Analysis; Edp Sciences; Astronomy And Astrophysics; 576; 2015; 1-29; A6
0004-6361
1432-0746
url http://hdl.handle.net/11336/15112
identifier_str_mv Perren, Gabriel Ignacio; Vazquez, Ruben Angel; Piatti, Andres Eduardo; ASteCA: Automated Stellar Cluster Analysis; Edp Sciences; Astronomy And Astrophysics; 576; 2015; 1-29; A6
0004-6361
1432-0746
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1051/0004-6361/201424946
info:eu-repo/semantics/altIdentifier/url/http://www.aanda.org/articles/aa/abs/2015/04/aa24946-14/aa24946-14.html
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
dc.publisher.none.fl_str_mv Edp Sciences
publisher.none.fl_str_mv Edp Sciences
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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