Gamma Ray AGNs: Estimating Redshifts and Blazar Classification using traditional Neural Networks with smart initialization and self-supervised learning

TL;DR

This paper introduces advanced neural network techniques, including smart initialization and self-supervised learning, to improve gamma-ray AGN redshift estimation and classification, addressing limitations of prior simple models.

Contribution

It presents a Bayesian model for uncertainty-aware redshift estimation and innovative initialization and self-supervised methods for AGN classification, advancing beyond basic existing algorithms.

Findings

Bayesian model effectively estimates redshifts with confidence levels

Smart initialization improves classification accuracy

Self-supervised algorithms enhance prediction robustness

Abstract

Redshift estimation and the classification of gamma-ray AGNs represent crucial challenges in the field of gamma-ray astronomy. Recent efforts have been made to tackle these problems using traditional machine learning methods. However, the simplicity of existing algorithms, combined with their basic implementations, underscores an opportunity and a need for further advancement in this area. Our approach begins by implementing a Bayesian model for redshift estimation, which can account for uncertainty while providing predictions with the desired confidence level. Subsequently, we address the classification problem by leveraging intelligent initialization techniques and employing soft voting. Additionally, we explore several potential self-supervised algorithms in their conventional form. Lastly, in addition to generating predictions for data with missing outputs, we ensure that the theoretical assertions put forth by both algorithms mutually reinforce each other.