# Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy

arXiv: https://arxiv.org/abs/1907.10480 · HTML: https://mlsanity.com/paper/1907.10480/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks](https://mlsanity.com/paper/2112.10168/) (2021)
- [Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks](https://mlsanity.com/paper/2112.15382/) (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)
- [Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks](https://mlsanity.com/paper/2112.10170/) (2021)

## Abstract (arXiv)

Deep learning techniques, namely convolutional neural networks (CNN), have previously been adapted to select gamma-ray events in the TAIGA experiment, having achieved a good quality of selection as compared with the conventional Hillas approach. Another important task for the TAIGA data analysis was also solved with CNN: gamma-ray energy estimation showed some improvement in comparison with the conventional method based on the Hillas analysis. Furthermore, our software was completely redeveloped for the graphics processing unit (GPU), which led to significantly faster calculations in both of these tasks. All the results have been obtained with the simulated data of TAIGA Monte Carlo software; their experimental confirmation is envisaged for the near future.