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A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars

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dc.contributor.authorDelgado-Ureña Poirier, Héctor
dc.contributor.directorSarro Baro, Luis Manuel
dc.date.accessioned2024-05-20T12:24:01Z
dc.date.available2024-05-20T12:24:01Z
dc.date.issued2014-02-27
dc.description.abstractThis 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.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14143
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleA Bayesian Graphical Model for Frequency Recovery of Periodic Variable Starses
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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