12-23 July 2021
Online
Europe/Berlin timezone

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

13 Jul 2021, 12:00
1h 30m
04

04

Talk GAI | Gamma Ray Indirect Discussion

Speaker

Thomas Vuillaume (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP)

Description

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.
IACTs 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.
In 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.
The 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.

Keywords

CTA; analysis; machine learning

Subcategory Experimental Methods & Instrumentation
Collaboration CTA

Primary authors

Thomas Vuillaume (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP) Dr Mikael Jacquemont (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP; LISTIC, Université Savoie Mont-Blanc ) Mr Mathieu de Bony de Lavergne (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP,) David Sanchez (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP) Vincent Poireau (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP) Dr Gilles Maurin (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP) Dr Alexandre Benoit (LISTIC, Université Savoie Mont-Blanc ) Prof. Patrick Lambert (LISTIC, Université Savoie Mont-Blanc ) Dr Giovanni Lamanna (Laboratoire d’Annecy de Physique des Particules, Univ. Grenoble Alpes, Univ. Savoie MontBlanc, CNRS, LAPP)

Co-author

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