Machine Learning in Astronomy: A Case Study in Quasar-Star Classification

TL;DR

This paper evaluates various machine learning methods for classifying stars and quasars in SDSS data, emphasizing the effectiveness of asymmetric AdaBoost and critically reviewing existing approaches.

Contribution

It provides a comprehensive review of ML classification techniques in astronomy and demonstrates the superior performance of asymmetric AdaBoost for star-quasar classification.

Findings

Asymmetric AdaBoost outperforms other ML methods in classification accuracy.

Critical analysis highlights pitfalls in previous ML approaches.

The study validates the suitability of specific ML methods for astronomical data.

Abstract

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We provide a careful scrutiny of approaches available in the literature and have highlighted the pitfalls in those approaches based on the nature of data used for the study. The aim is to investigate the appropriateness of the application of certain ML methods. The manuscript argues convincingly in favor of the efficacy of asymmetric AdaBoost to classify photometric data. The paper presents a critical review of existing study and puts forward an application of asymmetric AdaBoost, as an offspring of that exercise.