Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes

Abstract

Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity, gamma-ray showers must be efficiently distinguished from the dominant background of cosmic-ray showers using images from multiple telescopes. A combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) has been proposed to perform this task. Using CTLearn, an open source Python package using deep learning to analyze data from IACTs, with simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement a CNN-RNN network and find no evidence that sorting telescope images by total amplitude improves background rejection performance.

4 pages, 4 figures, Proceedings of the 2019 New York Scientific Data Summit (NYSDS)

References in corpus (4)

• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees
• Studying deep convolutional neural networks with hexagonal lattices for imaging atmospheric Cherenkov telescope event reconstruction
• CTLearn: Deep Learning for Gamma-ray Astronomy

Cited by in corpus (9)

• Deep Ensemble Analysis for Imaging X-ray Polarimetry
• Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
• Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes
• Machine Learning in Gamma Astronomy
• Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array
• Neural network analysis of X-ray polarimeter data
• Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes
• Ultra-Fast Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks
• Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks