EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||8|
|Section||T6 - Machine learning & analysis|
|Published online||17 September 2019|
GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LAPP,
2 Univ. Savoie Mont Blanc, CNRS, LISTIC, 74000 Annecy, France
3 Orobix, 24121 Bergamo BG, Italy
* e-mail: email@example.com
** e-mail: firstname.lastname@example.org
Published online: 17 September 2019
The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes for gamma-ray astronomy. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. Moreover, the vast majority of triggered events come from protons that represent a background for gamma-ray astronomy. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data in many domains. Thanks to the huge amount of simulated data and later of real data, produced by CTA, these algorithms look well-suited and very promising. Moreover, the trained neural networks show very good computing performances during execution. Here we present a first study of deep learning architectures applied to CTA simulated data to perform the reconstruction of the particles energy and incoming direction and the development of a specific framework, GammaLearn, to accomplish this task.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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