First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

TL;DR

This paper demonstrates a deep learning-based method for full-event reconstruction in gamma-ray astronomy using Cherenkov Telescope Array data, outperforming standard techniques on real and simulated data.

Contribution

It introduces the first full-event reconstruction approach with deep neural networks applied to real Cherenkov Telescope data, validating its effectiveness.

Findings

Deep learning outperforms standard analysis methods.

Method works on both simulated and real data.

Highlights challenges in transitioning from simulation to real data.

Abstract

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.