Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques

TL;DR

This paper presents a machine learning approach employing advanced initialization and self-supervised techniques to classify gamma-ray blazars into BL Lac and FSRQ types, improving simplicity and deployment efficiency.

Contribution

It introduces a novel machine learning classification method with minimal features and parameters, utilizing advanced initialization and self-supervised learning for gamma-ray source classification.

Findings

Classified 820 sources as BL Lacs

Classified 295 sources as FSRQs

Achieved high accuracy with simple model

Abstract

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray emitting sources, among which a significant number of objects have been identified as active galactic nuclei (AGN). The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialisation techniques and self-supervised learning for classifying blazars into BL Lacertae objects (BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be classified as FSRQs.