Machine Learning in Gamma Astronomy
Authors:
- 1 December 2024
- journal article
- Published by Allerton Press in Moscow University Physics Bulletin
- Vol. 79 (S2) , S690-S699
Abstract
The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric CherenkoKeywords
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This publication has 57 references indexed in Scilit:
- Using deep learning methods for IACT data analysis in gamma-ray astronomy: A reviewAstronomy and Computing, 2024
- Analysis Methods for Gamma-Ray Astronomy2023
- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation2020
- A NECTAr-based upgrade for the Cherenkov cameras of the H.E.S.S. 12-meter telescopesAstroparticle Physics, 2020
- Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes dataAstroparticle Physics, 2019
- Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data2018
- Neural Networks and Deep Learning2018
- Introducing the CTA conceptAstroparticle Physics, 2013
- A new analysis strategy for detection of faint γ-ray sources with Imaging Atmospheric Cherenkov TelescopesAstroparticle Physics, 2011
- γ/hadron separation in very-high-energy -ray astronomy using a multivariate analysis methodAstroparticle Physics, 2009