Analysis of the Cherenkov Telescope Array first Large Size Telescope real data using convolutional neural networks

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  • <p>ICRC</p>

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  • uploaded June 25, 2021

Discussion timeslot (ZOOM-Meeting): 13. July 2021 - 12:00
ZOOM-Meeting URL: https://desy.zoom.us/j/98542982538
ZOOM-Meeting ID: 98542982538
ZOOM-Meeting Passcode: ICRC2021
Corresponding Session: https://icrc2021-venue.desy.de/channel/52-Analysis-Methods-Catalogues-Community-Tools-Machine-Learning-GAD-GAI/64
Live-Stream URL: https://icrc2021-venue.desy.de/livestream/Discussion-04/5

Abstract:
'The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray observatory and will be composed of two arrays of imaging atmospheric Cherenkov telescopes (IACTs) located in the Northern and Southern hemispheres respectively. The first CTA prototype telescope built on-site, the Large Size Telescope (LST-1), is under commissioning in La Palma and has already taken data on numerous known sources. rnIACTs detect the faint flash of Cherenkov light indirectly produced after a very energetic gamma-ray photon has interacted with the atmosphere and generated an atmospheric shower. Reconstruction of the characteristics of the primary photons is usually done using a parameterization up to the third order of the light distribution of the images.rnIn order to go beyond this classical method, new approaches are being developed using state-of-the-art methods based on convolutional neural networks (CNN) to reconstruct the properties of each event (incoming direction, energy and particle type) directly from the telescope images. While promising, these methods are notoriously difficult to apply to real data due to differences (such as different levels of night sky background) between Monte Carlo (MC) data used to train the network and real data.rnThe GammaLearn project, based on these CNN approaches, has already shown an increase in sensitivity on MC simulations for LST-1 as well as a lower energy threshold. In this work, we apply the GammaLearn network to real data acquired by LST-1 and compare the results to the classical approach that uses random forests trained on extracted image parameters. The improvements on the background rejection, event direction, and energy reconstruction are discussed in this contribution.'

Authors: Thomas Vuillaume | Mikael Jacquemont | Mathieu de Bony de Lavergne | David Sanchez | Vincent Poireau | Gilles Maurin | Alexandre Benoit | Patrick Lambert | Giovanni Lamanna
Co-Authors: for the CTA LST project
Collaboration: CTA

Indico-ID: 272
Proceeding URL: https://pos.sissa.it/395/703

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Presenter: Mathieu de Bony de Lavergne

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