Machine Learning in Gamma Astronomy

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.

20 pages, 5 figures, Proceedings of The 8th International Conference on Deep Learning in Computational Physics, June 19-21, 2024, Moscow, Russia

References in corpus (15)

• Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes
• A NECTAr-based upgrade for the Cherenkov cameras of the H.E.S.S. 12-meter telescopes
• Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
• Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes
• Machine Learning for Imaging Cherenkov Detectors
• Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads
• Statistical Tools for Imaging Atmospheric Cherenkov Telescopes
• Deep learning techniques for Imaging Air Cherenkov Telescopes
• Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis
• Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods
• The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn
• Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes
• Analysis Methods for Gamma-ray Astronomy