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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/15112
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:43:19Zoai: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:34982025-09-29 10:43:19.478CONICET 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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13.070432 |