# Machine Learning in Gamma Astronomy

arXiv: https://arxiv.org/abs/2501.19064 · HTML: https://mlsanity.com/paper/2501.19064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array](https://mlsanity.com/paper/2103.06054/) (2021)
- [First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning](https://mlsanity.com/paper/2105.14927/) (2021)
- [Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods](https://mlsanity.com/paper/2211.08971/) (2022)
- [Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis](https://mlsanity.com/paper/2211.15807/) (2022)
- [Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes](https://mlsanity.com/paper/2211.12553/) (2022)

## Abstract (arXiv)

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.