Calibration of semi-analytic models of galaxy formation using particle swarm optimization

Autores
Ruiz, Andrés N.; Cora, Sofía Alejandra; Padilla, Nelson D.; Domínguez, Mariano J.; Vega Martínez, Cristian Antonio; Tecce, Tomás E.; Orsi, Álvaro; Yaryura, Yamila; García Lambas, Diego; Gargiulo, Ignacio Daniel; Muñoz Arancibia, Alejandra M.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
Facultad de Ciencias Astronómicas y Geofísicas
Instituto de Astrofísica de La Plata
Materia
Ciencias Astronómicas
galaxies: evolution
galaxies: formation
methods: numerical
methods: statistical
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/85874

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network_name_str SEDICI (UNLP)
spelling Calibration of semi-analytic models of galaxy formation using particle swarm optimizationRuiz, Andrés N.Cora, Sofía AlejandraPadilla, Nelson D.Domínguez, Mariano J.Vega Martínez, Cristian AntonioTecce, Tomás E.Orsi, ÁlvaroYaryura, YamilaGarcía Lambas, DiegoGargiulo, Ignacio DanielMuñoz Arancibia, Alejandra M.Ciencias Astronómicasgalaxies: evolutiongalaxies: formationmethods: numericalmethods: statisticalWe present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.Facultad de Ciencias Astronómicas y GeofísicasInstituto de Astrofísica de La Plata2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/85874enginfo:eu-repo/semantics/altIdentifier/issn/0004-637Xinfo:eu-repo/semantics/altIdentifier/doi/10.1088/0004-637X/801/2/139info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:16:55Zoai:sedici.unlp.edu.ar:10915/85874Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:16:55.567SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Calibration of semi-analytic models of galaxy formation using particle swarm optimization
title Calibration of semi-analytic models of galaxy formation using particle swarm optimization
spellingShingle Calibration of semi-analytic models of galaxy formation using particle swarm optimization
Ruiz, Andrés N.
Ciencias Astronómicas
galaxies: evolution
galaxies: formation
methods: numerical
methods: statistical
title_short Calibration of semi-analytic models of galaxy formation using particle swarm optimization
title_full Calibration of semi-analytic models of galaxy formation using particle swarm optimization
title_fullStr Calibration of semi-analytic models of galaxy formation using particle swarm optimization
title_full_unstemmed Calibration of semi-analytic models of galaxy formation using particle swarm optimization
title_sort Calibration of semi-analytic models of galaxy formation using particle swarm optimization
dc.creator.none.fl_str_mv Ruiz, Andrés N.
Cora, Sofía Alejandra
Padilla, Nelson D.
Domínguez, Mariano J.
Vega Martínez, Cristian Antonio
Tecce, Tomás E.
Orsi, Álvaro
Yaryura, Yamila
García Lambas, Diego
Gargiulo, Ignacio Daniel
Muñoz Arancibia, Alejandra M.
author Ruiz, Andrés N.
author_facet Ruiz, Andrés N.
Cora, Sofía Alejandra
Padilla, Nelson D.
Domínguez, Mariano J.
Vega Martínez, Cristian Antonio
Tecce, Tomás E.
Orsi, Álvaro
Yaryura, Yamila
García Lambas, Diego
Gargiulo, Ignacio Daniel
Muñoz Arancibia, Alejandra M.
author_role author
author2 Cora, Sofía Alejandra
Padilla, Nelson D.
Domínguez, Mariano J.
Vega Martínez, Cristian Antonio
Tecce, Tomás E.
Orsi, Álvaro
Yaryura, Yamila
García Lambas, Diego
Gargiulo, Ignacio Daniel
Muñoz Arancibia, Alejandra M.
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Astronómicas
galaxies: evolution
galaxies: formation
methods: numerical
methods: statistical
topic Ciencias Astronómicas
galaxies: evolution
galaxies: formation
methods: numerical
methods: statistical
dc.description.none.fl_txt_mv We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
Facultad de Ciencias Astronómicas y Geofísicas
Instituto de Astrofísica de La Plata
description We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
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
Articulo
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://sedici.unlp.edu.ar/handle/10915/85874
url http://sedici.unlp.edu.ar/handle/10915/85874
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/0004-637X
info:eu-repo/semantics/altIdentifier/doi/10.1088/0004-637X/801/2/139
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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