Reconstruction of stereoscopic CTA events using deep learning with CTLearn

TL;DR

This paper demonstrates a deep learning approach for reconstructing stereoscopic Cherenkov Telescope Array events, improving the analysis of gamma-ray air showers through a novel, data-driven method using CTLearn.

Contribution

It introduces a deep learning-based full-event reconstruction method for CTA data utilizing CTLearn, advancing gamma-ray event analysis with pixel-wise camera data.

Findings

Deep learning enables accurate event reconstruction in CTA simulations.

The method leverages pixel-wise camera data for improved analysis.

Results show potential for enhanced gamma-ray source detection.

Abstract

The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input.