Advanced Astroinformatics for Variable Star Classification

TL;DR

This paper presents a comprehensive machine learning-based methodology for classifying variable stars using light curve data, emphasizing system development, performance analysis, and readiness for big data from future telescopes like LSST.

Contribution

It introduces an end-to-end system development strategy for variable star classification, integrating feature extraction, detection, classification, and performance analysis.

Findings

Developed a complete classification system for variable stars.

Emphasized the importance of performance analysis in machine learning pipelines.

Prepared methodologies for handling large-scale astronomical data from next-generation telescopes.

Abstract

This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and how this deluge of information can be studied in order to improve our understanding of the universe. While our focus will be on the development of machine learning methodologies for the identification of variable star type based on light curve data and associated information, one of the goals of this work is the acknowledgment that the development of a true machine learning methodology must include not only study of what goes into the service (features, optimization methods) but a study on how we understand what comes out of the service (performance analysis). The complete development of a beginning-to-end system development strategy is presented as the following individual developments (simulation, training, feature extraction, detection, classification, and performance analysis). We propose that a complete machine learning strategy for use in the upcoming era of big data from the next generation of big telescopes, such as LSST, must consider this type of design integration.