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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/85874
Ver los metadatos del registro completo
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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 |
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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 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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