Machine Learning in Gamma Astronomy

IF 0.4 4区物理与天体物理 Q4 PHYSICS, MULTIDISCIPLINARY

Moscow University Physics Bulletin Pub Date : 2025-03-22 DOI:10.3103/S002713492470214X

A. P. Kryukov, A. P. Demichev, V. A. Ilyin

引用次数: 0

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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伽马天文学中的机器学习

本文旨在回顾用于分析大气切伦科夫成像望远镜获得的天体粒子数据的最流行的深度学习方法，并提供原始论文参考文献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Moscow University Physics Bulletin PHYSICS, MULTIDISCIPLINARY-

CiteScore

0.70

自引率

0.00%

发文量

129

审稿时长

6-12 weeks

期刊介绍： Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.