A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars

This thesis has been developed in the context of the recently launched European Space Agency’s Gaia mission. The thesis has addressed the problem of determining the probability distributions of the real physical parameters for a variable star population, given their recovered values by the Data Proc...

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Detalles Bibliográficos
Autor: Delgado, Hector E.
Tipo de recurso: tesis de maestría
Fecha de publicación:2014
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/14143
Acceso en línea:https://hdl.handle.net/20.500.14468/14143
Access Level:acceso abierto
Palabra clave:1203.04 Inteligencia artificial
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spelling A Bayesian Graphical Model for Frequency Recovery of Periodic Variable StarsDelgado, Hector E.1203.04 Inteligencia artificialThis thesis has been developed in the context of the recently launched European Space Agency’s Gaia mission. The thesis has addressed the problem of determining the probability distributions of the real physical parameters for a variable star population, given their recovered values by the Data Processing and Analysis Consortium (DPAC) from the telemetry of the satellite. These recovered values are affected by a number of stochastic errors and systematic biases due to the aliasing phenomenon as a product of the Gaia scanning law, the optical and photometric resolution of the satellite and the algorithms used in the recovery process. The purpose of the thesis has been to model the data recovery process and infer the real distributions for the frequencies, apparent Gmagnitudes and amplitudes for a Large Magellanic Cloud (LMC) classic Cepheid star population. A two level Bayesian graphical model was constructed with the aid of a domain expert to model the recovery process and a Markov chain Monte Carlo (MCMC) algorithm specified to perform the inference. The system was implemented in the declarative BUGS language. The system was trained from a set of recovered data from an artificially generated real distribution of LMC Cepheids. The system was tested by comparing the parameters of the artificially generated real distributions with the distributions inferred by the MCMC algorithm. The results obtained have shown that the system remove successfully the systematic biases and is able to infer correctly the real frequency distribution. The results have also shown a correct inference for the real apparent magnitudes in the G band. Nevertheless, the results obtained for the case of the real amplitude distribution have not allowed to establish significant conclusions.Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Sarro Baro, Luis Manuele-Spacio UNED20242024-05-2020142014-02-2720142014-02-27master thesishttp://purl.org/coar/resource_type/c_bdccinfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/20.500.14468/14143reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/141432026-06-06T12:38:31Z
dc.title.none.fl_str_mv A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
title A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
spellingShingle A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
Delgado, Hector E.
1203.04 Inteligencia artificial
title_short A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
title_full A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
title_fullStr A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
title_full_unstemmed A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
title_sort A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
dc.creator.none.fl_str_mv Delgado, Hector E.
author Delgado, Hector E.
author_facet Delgado, Hector E.
author_role author
dc.contributor.none.fl_str_mv Sarro Baro, Luis Manuel
e-Spacio UNED
dc.subject.none.fl_str_mv 1203.04 Inteligencia artificial
topic 1203.04 Inteligencia artificial
description This thesis has been developed in the context of the recently launched European Space Agency’s Gaia mission. The thesis has addressed the problem of determining the probability distributions of the real physical parameters for a variable star population, given their recovered values by the Data Processing and Analysis Consortium (DPAC) from the telemetry of the satellite. These recovered values are affected by a number of stochastic errors and systematic biases due to the aliasing phenomenon as a product of the Gaia scanning law, the optical and photometric resolution of the satellite and the algorithms used in the recovery process. The purpose of the thesis has been to model the data recovery process and infer the real distributions for the frequencies, apparent Gmagnitudes and amplitudes for a Large Magellanic Cloud (LMC) classic Cepheid star population. A two level Bayesian graphical model was constructed with the aid of a domain expert to model the recovery process and a Markov chain Monte Carlo (MCMC) algorithm specified to perform the inference. The system was implemented in the declarative BUGS language. The system was trained from a set of recovered data from an artificially generated real distribution of LMC Cepheids. The system was tested by comparing the parameters of the artificially generated real distributions with the distributions inferred by the MCMC algorithm. The results obtained have shown that the system remove successfully the systematic biases and is able to infer correctly the real frequency distribution. The results have also shown a correct inference for the real apparent magnitudes in the G band. Nevertheless, the results obtained for the case of the real amplitude distribution have not allowed to establish significant conclusions.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-02-27
2014
2014-02-27
2024
2024-05-20
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/14143
url https://hdl.handle.net/20.500.14468/14143
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
publisher.none.fl_str_mv Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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