STAR CLUSTER MEMBERSHIP IDENTIFICATION BY SUPERVISED MACHINE LEARNING MODELS APPLIED TO N-BODY SIMULATIONS

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dc.contributor.authorBissekenov, Abylay
dc.date.accessioned2023-06-01T08:10:02Z
dc.date.available2023-06-01T08:10:02Z
dc.date.issued2023
dc.description.abstractThis thesis investigates possible ways to apply supervised machine learning algorithms on N-body simulations. Because of the limitations of observational data, there is a motivation to research star clusters by the N-body simulations. The simulations used for the study are based on the Plummer model, and each has its star formation efficiency (SFE) and several random realizations. A random forest model was trained on the simulation with 15% star formation efficiency on a 20-100 Myr timeframe. The model was tested on the other N-body simulations with 17-25% SFEs and showed high classification accuracy throughout the whole dynamic evolution of tested simulations. The majority of mistakes of the model were the false positives (FP) that turned out to be within a 2 Jacobi radius, indicating that they might be gravitationally bounded to center of cluster. Framework and learning strategy can be considered effective and further applied for the mock observations of N-body simulations.en_US
dc.identifier.citationBissekenov, A. (2023). Star Cluster Membership Identification By Supervised Machine Learning Models Applied To N-Body Simulations. School of Sciences and Humanitiesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7167
dc.language.isoenen_US
dc.publisherSchool of Sciences and Humanitiesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Embargoen_US
dc.subjectStar clustersen_US
dc.subjectN-body simulationen_US
dc.subjectMachine Learningen_US
dc.subjectSupervised Learningen_US
dc.titleSTAR CLUSTER MEMBERSHIP IDENTIFICATION BY SUPERVISED MACHINE LEARNING MODELS APPLIED TO N-BODY SIMULATIONSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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