An Assessment of Machine Learning Algorithms as Applied to Astronomical Data Sets
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Physics and Astronomy
Date of Award
Fall 2022
Abstract
As astronomical datasets increase in scale, the analysis of data becomes less feasible to perform “by hand.” At scales when this becomes an issue, the use of computerized analysis becomes a greater necessity. Modern astronomical surveys are producing terabytes of data available for analysis by the scientific community. Machine learning (ML) algorithms are increasingly being used to classify objects within this data to draw conclusions from the dataset as a whole, but there is a large and growing set of ML algorithms to choose from, and astronomers as a general rule are not familiar with these techniques. This project will explore the efficacy of several the currently available supervised machine learning algorithms to identify and recommend the top choices for researchers more interested in astronomy than in computer science.
Advisor
Matt A. Wood
Subject Categories
Astrophysics and Astronomy | Computer Sciences | Physical Sciences and Mathematics
Recommended Citation
Biswas, Paul, "An Assessment of Machine Learning Algorithms as Applied to Astronomical Data Sets" (2022). Electronic Theses & Dissertations. 1041.
https://digitalcommons.tamuc.edu/etd/1041
