Reconstruction of stereoscopic CTA events using deep learning with CTLearn

Collapse authors list.
Miener, T, Nieto, D, Brill, A, Spencer, S, Contreras, JL, Abdalla, H, Abe, H, Abe, S, Abusleme, A, Acero, F et al (show 90 more authors) , Acharyya, A, Acín Portella, V, Ackley, K, Adam, R, Adams, C, Adhikari, SS, Aguado-Ruesga, I, Agudo, I, Aguilera, R, Aguirre-Santaella, A, Aharonian, F, Alberdi, A, Alfaro, R, Alfaro, J, Alispach, C, Aloisio, R, Alves Batista, R, Amans, JP, Amati, L, Amato, E, Ambrogi, L, Ambrosi, G, Ambrosio, M, Ammendola, R, Anderson, J, Anduze, M, Angüner, EO, Antonelli, LA, Antonuccio, V, Antoranz, P, Anutarawiramkul, R, Aragunde Gutierrez, J, Aramo, C, Araudo, A, Araya, M, Arbet-Engels, A, Arcaro, C, Arendt, V, Armand, C, Armstrong, T, Arqueros, F, Arrabito, L, Arsioli, B, Artero, M, Asano, K, Ascasíbar, Y, Aschersleben, J, Ashley, M, Attinà, P, Aubert, P, Singh, CB, Baack, D, Babic, A, Backes, M, Baena, V, Bajtlik, S, Baktash, A, Balazs, C, Balbo, M, Ballester, O, Ballet, J, Balmaverde, B, Bamba, A, Bandiera, R, Baquero Larriva, A, Barai, P, Barbier, C, Barbosa Martins, V, Barcelo, M, Barkov, M, Barnard, M, Baroncelli, L, Barres de Almeida, U, Barrio, JA, Bastieri, D, Batista, PI, Batkovic, I, Bauer, C, Bautista-González, R, Baxter, J, Becciani, U, Becerra González, J, Becherini, Y, Beck, G, Becker Tjus, J, Bednarek, W, Belfiore, A, Bellizzi, L, Belmont, R and Benbow, W (2022) Reconstruction of stereoscopic CTA events using deep learning with CTLearn. .

PDF 2109.05809.pdf - Other Download (4MB) | Preview

Abstract

The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	16 Mar 2023 09:09
Last Modified:	16 Mar 2023 09:09
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169097