Calibration of semi-analytic models of galaxy formation using particle swarm optimization
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-le...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2015
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/85874 |
| Aporte de: |
| id |
I19-R120-10915-85874 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Astronómicas galaxies: evolution galaxies: formation methods: numerical methods: statistical |
| spellingShingle |
Ciencias Astronómicas galaxies: evolution galaxies: formation methods: numerical methods: statistical 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. Calibration of semi-analytic models of galaxy formation using particle swarm optimization |
| topic_facet |
Ciencias Astronómicas galaxies: evolution galaxies: formation methods: numerical methods: statistical |
| 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. |
| format |
Articulo Articulo |
| author |
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_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_sort |
Ruiz, Andrés N. |
| title |
Calibration of semi-analytic models of galaxy formation using particle swarm optimization |
| 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 |
| publishDate |
2015 |
| url |
http://sedici.unlp.edu.ar/handle/10915/85874 |
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