STAR CLUSTER MEMBERSHIP IDENTIFICATION BY SUPERVISED MACHINE LEARNING MODELS APPLIED TO N-BODY SIMULATIONS
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Date
2023
Authors
Bissekenov, Abylay
Journal Title
Journal ISSN
Volume Title
Publisher
School of Sciences and Humanities
Abstract
This 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.
Description
Keywords
Type of access: Embargo, Star clusters, N-body simulation, Machine Learning, Supervised Learning
Citation
Bissekenov, A. (2023). Star Cluster Membership Identification By Supervised Machine Learning Models Applied To N-Body Simulations. School of Sciences and Humanities