First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning
This work addresses the challenge of analyzing gamma-ray astronomy data from the Cherenkov Telescope Array, representing a domain-specific advancement in applying deep learning to real telescope data.
The authors tackled the problem of reconstructing full events from real data of the Large Size Telescope 1 using deep convolutional neural networks, achieving performance that outperforms the standard analysis on both simulated and real data, thus validating the deep learning approach for CTA data analysis.
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.