Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data

TL;DR

This paper demonstrates that convolutional neural networks can improve background rejection in gamma-ray data analysis from Imaging Atmospheric Cherenkov Telescopes, showing promising results for future astrophysical observations.

Contribution

The study introduces a novel CNN-based analysis scheme for IACT data, including methods to handle hexagonal pixel arrangements, and compares its performance to existing algorithms.

Findings

CNNs outperform traditional algorithms in background rejection

Direction reconstruction with CNNs is comparable to current methods

Proof-of-concept for CNN application in IACT data analysis

Abstract

Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately determine the energy of the recorded gamma-ray and the position of its source in the sky. We present results for background rejection and signal direction reconstruction from first studies of a novel data analysis scheme for IACT measurements. The new analysis is based on a set of Convolutional Neural Networks (CNNs) applied to images from the four H.E.S.S. phase-I telescopes. As the H.E.S.S. cameras pixels are arranged in a hexagonal array, we demonstrate two ways to use such image data to train CNNs: by resampling the images to a square grid and by applying modified convolution kernels that conserve the hexagonal grid properties. The networks were trained on sets of Monte-Carlo simulated events and tested on both simulations and measured data from the H.E.S.S. array. A comparison between the CNN analysis to current state-of-the-art algorithms reveals a clear improvement in background rejection performance. When applied to H.E.S.S. observation data, the CNN direction reconstruction performs at a similar level as traditional methods. These results serve as a proof-of-concept for the application of CNNs to the analysis of events recorded by IACTs.