Energy Reconstruction in Analysis of Cherenkov Telescopes Images in TAIGA Experiment Using Deep Learning Methods

Abstract

Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical complex allow to observe high energy gamma radiation helping to study many astrophysical objects and processes. TAIGA-IACT enables us to select gamma quanta from the total cosmic radiation flux and recover their primary parameters, such as energy and direction of arrival. The traditional method of processing the resulting images is an image parameterization - so-called the Hillas parameters method. At the present time Machine Learning methods, in particular Deep Learning methods have become actively used for IACT image processing. This paper presents the analysis of simulated Monte Carlo images by several Deep Learning methods for a single telescope (mono-mode) and multiple IACT telescopes (stereo-mode). The estimation of the quality of energy reconstruction was carried out and their energy spectra were analyzed using several types of neural networks. Using the developed methods the obtained results were also compared with the results obtained by traditional methods based on the Hillas parameters.

References in corpus (5)

• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• Deep Learning for Energy Estimation and Particle Identification in Gamma-ray Astronomy
• Cosmology with Very-High-Energy Gamma Rays
• The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks
• Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks

Cited by in corpus (3)

• Machine Learning in Gamma Astronomy
• Neural Networks for the Analysis of Traced Particles in Kinetic Plasma Simulations
• Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks