Reconstruction of stereoscopic CTA events using deep learning with CTLearn
Miener, T. ; Nieto, D. ; Brill, A. ; Spencer, S. ; Carlile, C. LU ; Dravins, D. LU
and Zmija, A.
(2022)
In Proceedings of Science
395.
- 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... (More)
- 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. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/bf657ed0-7f8a-4556-9b12-986a41653bcd
- author
- Miener, T.
; Nieto, D.
; Brill, A.
; Spencer, S.
; Carlile, C.
LU
; Dravins, D.
LU
and Zmija, A.
- author collaboration
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Cosmic rays, Cosmology, Deep learning, Gamma rays, Germanium alloys, Germanium compounds, Stereo image processing, Telescopes, Tellurium compounds, Air showers, Cherenkov telescope arrays, Current generation, Energy, Gamma ray observatories, Ground based, High energy gamma rays, Imaging atmospheric Cherenkov telescopes, International projects, Very high energies, Cameras
- host publication
- 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect
- series title
- Proceedings of Science
- volume
- 395
- article number
- 730
- external identifiers
-
- scopus:85145019293
- ISSN
- 1824-8039
- DOI
- 10.22323/1.395.0730
- language
- English
- LU publication?
- yes
- id
- bf657ed0-7f8a-4556-9b12-986a41653bcd
- date added to LUP
- 2023-01-16 12:16:15
- date last changed
- 2024-04-03 18:18:59
@inproceedings{bf657ed0-7f8a-4556-9b12-986a41653bcd,
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. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)}},
author = {{Miener, T. and Nieto, D. and Brill, A. and Spencer, S. and Carlile, C. and Dravins, D. and Zmija, A.}},
booktitle = {{37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect}},
issn = {{1824-8039}},
keywords = {{Cosmic rays; Cosmology; Deep learning; Gamma rays; Germanium alloys; Germanium compounds; Stereo image processing; Telescopes; Tellurium compounds; Air showers; Cherenkov telescope arrays; Current generation; Energy; Gamma ray observatories; Ground based; High energy gamma rays; Imaging atmospheric Cherenkov telescopes; International projects; Very high energies; Cameras}},
language = {{eng}},
series = {{Proceedings of Science}},
title = {{Reconstruction of stereoscopic CTA events using deep learning with CTLearn}},
url = {{http://dx.doi.org/10.22323/1.395.0730}},
doi = {{10.22323/1.395.0730}},
volume = {{395}},
year = {{2022}},
}