Machine Learning in Gamma Astronomy
A.P.Kryukov, A.P.Demichev, V.A.Ilyin
TL;DR
This paper reviews deep learning techniques applied to gamma-ray astronomy data from Imaging Atmospheric Cherenkov Telescopes, summarizing current methods and providing references for further study.
Contribution
It offers a comprehensive overview of deep learning applications in gamma-ray astronomy, highlighting recent advances and key references.
Findings
Deep learning improves gamma-ray data analysis accuracy
Various neural network architectures are employed in the field
The review consolidates current state-of-the-art methods
Abstract
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.
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