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

2020/01/10 by Aryeh Brill, Qi Feng, T. Brian Humensky +3 · 1 citation
Physics and Astronomy · #astro-ph.IM

paper · pdf · doi:10.1109/nysds.2019.8909697

published as Proceedings of the 2019 New York Scientific Data Summit (NYSDS), 12-14 June 2019, New York, NY, USA. Available: IEEE Xplore, http://www.ieee.org · 4 pages, 4 figures, Proceedings of the 2019 New York Scientific Data Summit (NYSDS)

arxiv created 2020/01/10 · arxiv updated 2020/01/13

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.

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• Enhancing event reconstruction for γ-ray particle detector arrays using transformers

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• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• CTLearn: Deep Learning for Gamma-ray Astronomy
• IACT event analysis with the MAGIC telescopes using deep convolutional neural networks with CTLearn
• First IACT Waveform Analysis Based on Deep Convolutional Neural Networks Using CTLearn
• The performance of the MAGIC telescopes using deep convolutional neural networks with CTLearn