Capability of using the normalizing flows for extraction rare gamma events in the TAIGA experiment

TL;DR

This paper explores the use of normalizing flows combined with deep learning for detecting rare gamma-ray events in the TAIGA experiment, demonstrating potential but currently inferior performance compared to existing methods.

Contribution

It introduces a novel anomaly detection approach using normalizing flows for gamma event detection in astrophysics data.

Findings

Potential for gamma detection demonstrated

Performance currently below existing methods

Proposed improvements for future implementation

Abstract

The objective of this work is to develop a method for detecting rare gamma quanta against the background of charged particles in the fluxes from sources in the Universe with the help of the deep learning and normalizing flows based method designed for anomaly detection. It is shown that the suggested method has a potential for the gamma detection. The method was tested on model data from the TAIGA-IACT experiment. The obtained quantitative performance indicators are still inferior to other approaches, and therefore possible ways to improve the implementation of the method are proposed.