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Colin Carlile, researcher at Lund Observatory. Photo.

Colin Carlile

Guest researcher

Colin Carlile, researcher at Lund Observatory. Photo.

Reconstruction of stereoscopic CTA events using deep learning with CTLearn

Author

  • T. Miener
  • D. Nieto
  • A. Brill
  • S. Spencer
  • C. Carlile
  • D. Dravins
  • A. Zmija

Summary, in English

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)

Department/s

  • Lund Observatory - Undergoing reorganization

Publishing year

2022

Language

English

Publication/Series

Proceedings of Science

Volume

395

Document type

Conference paper

Topic

  • Astronomy, Astrophysics and Cosmology

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 1824-8039