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Sökning: onr:"swepub:oai:lup.lub.lu.se:bf657ed0-7f8a-4556-9b12-986a41653bcd" > Reconstruction of s...

Reconstruction of stereoscopic CTA events using deep learning with CTLearn

Miener, T. (författare)
Complutense University of Madrid
Nieto, D. (författare)
Complutense University of Madrid
Brill, A. (författare)
Columbia University
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Spencer, S. (författare)
University of Oxford
Carlile, C. (författare)
Lund University,Lunds universitet,Astronomi - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Lund Observatory - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
Dravins, D. (författare)
Lund University,Lunds universitet,Astronomi - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Lund Observatory - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
Zmija, A. (författare)
Friedrich-Alexander University Erlangen-Nürnberg
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 (creator_code:org_t)
 
Trieste, Italy : Sissa Medialab, 2022
2022
Engelska.
Ingår i: 37th International Cosmic Ray Conference (ICRC2021) - GAI - Gamma Ray Indirect. - Trieste, Italy : Sissa Medialab. - 1824-8039. ; 395
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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)

Ämnesord

NATURVETENSKAP  -- Fysik -- Astronomi, astrofysik och kosmologi (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences -- Astronomy, Astrophysics and Cosmology (hsv//eng)

Nyckelord

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

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