Speakers
Description
The Cherenkov Telescope Array (CTA) will be the world's largest and the most sensitive ground-based gamma-ray observatory in the energy range from a few tens of GeV to tens of TeV. To achieve the best performance in such a wide range of primary gamma-ray photon energies, the whole array will consist of Imaging Atmospheric Cherenkov Telescopes of three different sizes. The Large-Sized Telescopes (LSTs) are designed to be the most sensitive in the low-energy band of CTA. The LST-1 prototype, currently in the commissioning phase, was inaugurated in October 2018 in La Palma (Spain) and it is the first of the four largest CTA telescopes, that will be built in the northern site of CTA.
The Random Forest method is currently employed in the reconstruction of the first data from the LST-1. This method, however, requires extensive preprocessing that includes signal integration and image parametrization, leading to the loss of information contained in the shower images. In this contribution, we will present a full-image reconstruction method using Inception deep convolutional neural network (CNN) applied on non-parametrized shower images. First, we will evaluate the performance of optimized networks on Monte Carlo simulations of LST-1 shower images, and compare the results with the performance of the standard reconstruction method. We will also show how both methods compare to real-data reconstruction.
