A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars

Delgado-Ureña Poirier, Héctor. (2014). A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.

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Título A Bayesian Graphical Model for Frequency Recovery of Periodic Variable Stars
Autor(es) Delgado-Ureña Poirier, Héctor
Abstract 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.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
Director/Tutor Sarro Baro, Luis Manuel
Fecha 2014-02-27
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Hdelgado
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Hdelgado
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Wed, 14 Jul 2021, 17:28:58 CET