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Comparative Analysis of Machine Learning Algorithms for Member Classification in Stellar Systems

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dc.contributor.authorNieto Petinal, Isabel
dc.contributor.directorOlivares Romero, Javier
dc.contributor.directorSarro Baro, Luis Manuel
dc.date.accessioned2025-10-10T10:42:42Z
dc.date.available2025-10-10T10:42:42Z
dc.date.issued2024-09-01
dc.description.abstractContext. Investigating the formation of open clusters is essential for understanding the evolution of the MilkyWay’s stellar content. Various machine learning algorithms have been implemented to identify members within these clusters, however, the performance of these methods has not been properly analyzed. Aims. The main objective is to analyze and evaluate various algorithms and metrics used for membership identification in open clusters of stellar systems. This includes a detailed assessment of the performance, time and objectives of the algorithms in two distinct experiments. Methods. The analysis of key algorithms such as HDBSCAN, StarGO, pyUPMASK and Miec using two different synthetic clusters that mimic the characteristics of Gaia catalog. The experiments differ in the number of objects, as the algorithms’ performance can be highly dependent on this factor. Results. For numerous clusters, the best algorithm depended on the objectives of the classification: HDBSCAN for fast computing, pyUPMASK for complete selection of the members and Miec for a more balanced approach. For smaller clusters, Miec was the best algorithm in all metrics. Similarly, there is no best metric for all cases. Similarly, no single metric is ideal for all situations. For cases requiring a clean selection of members, contamination rate and PPV are recommended. For a more complete list of cluster members, TPR is useful, while MCC and LSR offer a more balanced approach. Conclusions. The performance of the algorithm and the metrics selected depend on the objectives of the classification itself. Therefore, there is no universally best algorithm or metric, so it’s important to evaluate the goals and select the most suitable accordingly.es
dc.identifier.citationNieto Petinal, Isabel. Trabajo Fin de Máster: Comparative Analysis of Machine Learning Algorithms for Member Classification in Stellar Systems. Universidad Nacional de Educación a Distancia (UNED), 2024
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30371
dc.language.isoen
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Inteligencia Artificial
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsmethodsen
dc.subject.keywordsalgorithm and metric analysisen
dc.subject.keywordsopen clusters and associations: generalen
dc.subject.keywordsastrometryen
dc.subject.keywordsphotometryen
dc.titleComparative Analysis of Machine Learning Algorithms for Member Classification in Stellar Systemsen
dc.titleAnálisis comparativo de algoritmos de aprendizaje automático para la clasificación de miembros de sistemas estelareses
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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