An investigation into the role of machine learning and deep learning models as a means of leveraging the ever-expanding volume of astronomical data to automate stellar classification.

Author

Gerard Heraghty, CCT College Dublin

Supervisor

David Gonzalez

Programme

MSc in Data Analytics

Abstract

This research investigates the use of machine learning and neural network models for automated stellar classification in large astronomical surveys, addressing challenges posed by the increasing volume of data. Using the MK scheme as the classification standard, the study focused on spectroscopic data and balanced the dataset using SMOTE techniques to handle class imbalances. Various models, including Random Forest, SVM, MLP, and CNN, were trained and compared for classifying MK main and sub-classes. CNN achieved the highest accuracy (93.86%) for main class classification, while SVM excelled at sub-class classification (63.23%) on balanced datasets. However, when tested on real-world SDSS data, the models showed limited generalisability, highlighting the need for further refinement.

Date of Award

2024

Full Publication Date

2024

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

Recommended Citation

Heraghty, Gerard, "An investigation into the role of machine learning and deep learning models as a means of leveraging the ever-expanding volume of astronomical data to automate stellar classification." (2024). ICT. 51.
https://arc.cct.ie/ict/51