Full LST-1 data reconstruction with the use of convolutional neural networks. (arXiv:2111.14478v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Jurysek_J/0/1/0/all/0/1">Jakub Juryšek</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lyard_E/0/1/0/all/0/1">Etienne Lyard</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Walter_R/0/1/0/all/0/1">Roland Walter</a> (for the CTA-LST Project)
The Cherenkov Telescope Array (CTA) will be the world’s largest and most
sensitive ground-based gamma-ray observatory in the energy range from a few
tens of GeV to tens of TeV. The LST-1 prototype, currently in its commissioning
phase, is the first of the four largest CTA telescopes, that will be built in
the northern site of CTA in La Palma, Canary Islands, Spain. In this
contribution, we present a full-image reconstruction method using a modified
InceptionV3 deep convolutional neural network applied on non-parametrized
shower images. We evaluate the performance of optimized networks on Monte Carlo
simulations of LST-1 shower images, and compare the results with the
performance of the standard reconstruction method. We also show how both
methods work on real-data reconstruction.
The Cherenkov Telescope Array (CTA) will be the world’s largest and most
sensitive ground-based gamma-ray observatory in the energy range from a few
tens of GeV to tens of TeV. The LST-1 prototype, currently in its commissioning
phase, is the first of the four largest CTA telescopes, that will be built in
the northern site of CTA in La Palma, Canary Islands, Spain. In this
contribution, we present a full-image reconstruction method using a modified
InceptionV3 deep convolutional neural network applied on non-parametrized
shower images. We evaluate the performance of optimized networks on Monte Carlo
simulations of LST-1 shower images, and compare the results with the
performance of the standard reconstruction method. We also show how both
methods work on real-data reconstruction.
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