The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Colin Carlile, researcher at Lund Observatory. Photo.

Colin Carlile

Guest researcher

Colin Carlile, researcher at Lund Observatory. Photo.

Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data

Author

  • J. Aschersleben
  • R. F. Peletier
  • M. Vecchi
  • M. H. F. Wilkinson
  • C. Carlile
  • D. Dravins
  • A. Zmija

Summary, in English

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © 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

  • Convolution
  • Cosmic rays
  • Cosmology
  • Feature extraction
  • Neural networks
  • Particle size analysis
  • Telescopes
  • Cherenkov emissions
  • Cherenkov telescope arrays
  • Convolutional neural network
  • Current generation
  • Gamma ray observatories
  • Gamma ray telescope
  • Gamma-rays
  • Ground level
  • Pattern spectrum
  • Very high energies
  • Gamma rays

Conference name

37th International Cosmic Ray Conference

Conference date

2021-07-12 - 2021-07-23

Conference place

Berlin, Germany

Status

Published

ISBN/ISSN/Other

  • ISSN: 1824-8039