First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning
arXiv:2105.14927 · doi:10.1109/CBMI50038.2021.9461918
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.
References in corpus (7)
- A high performance likelihood reconstruction of gamma-rays for Imaging Atmospheric Cherenkov Telescopes
- Simulation of Imaging Atmospheric Cherenkov Telescopes with CORSIKA and sim_telarray
- Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
- Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks
- Optimization of multivariate analysis for IACT stereoscopic systems
- SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
- Reconstruction of IACT events using deep learning techniques with CTLearn