Extracting gamma-ray information from images with convolutional neural network methods on simulated Cherenkov Telescope Array data

Abstract

The Cherenkov Telescope Array (CTA) will be the world's leading ground-based gamma-ray observatory allowing us to study very high energy phenomena in the Universe. CTA will produce huge data sets, of the order of petabytes, and the challenge is to find better alternative data analysis methods to the already existing ones. Machine learning algorithms, like deep learning techniques, give encouraging results in this direction. In particular, convolutional neural network methods on images have proven to be effective in pattern recognition and produce data representations which can achieve satisfactory predictions. We test the use of convolutional neural networks to discriminate signal from background images with high rejections factors and to provide reconstruction parameters from gamma-ray events. The networks are trained and evaluated on artificial data sets of images. The results show that neural networks trained with simulated data can be useful to extract gamma-ray information. Such networks would help us to make the best use of large quantities of real data coming in the next decades.

arXiv comment12 pages, 7 figures, ANNPR 2018 conference, 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition

References in corpus (21)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• An overview of gradient descent optimization algorithms
• A high performance likelihood reconstruction of gamma-rays for Imaging Atmospheric Cherenkov Telescopes
• The First VERITAS Telescope
• Simulation of Imaging Atmospheric Cherenkov Telescopes with CORSIKA and sim_telarray
• Performance of the MAGIC stereo system obtained with Crab Nebula data
• Monte Carlo design studies for the Cherenkov Telescope Array
• Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
• A Monte Carlo Template based analysis for Air-Cherenkov Arrays
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• The analysis of VERITAS muon images using convolutional neural networks
• Harmonic Networks: Deep Translation and Rotation Equivariance
• Photon Reconstruction for H.E.S.S. Using a Semi-Analytical Shower Model
• Probing Convolutional Neural Networks for Event Reconstruction in γ-Ray Astronomy with Cherenkov Telescopes
• Bounded Rational Decision-Making with Adaptive Neural Network Priors
• Timing analysis techniques at large core distances for multi-TeV gamma ray astronomy
• Estimation of the height of the first interaction in gamma-ray showers observed by Cherenkov telescopes
• Cherenkov Telescope Array Status Report

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• Deep learning techniques for Imaging Air Cherenkov Telescopes
• Deep unsupervised domain adaptation applied to the Cherenkov Telescope Array Large-Sized Telescope
• Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes