Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

J. Aschersleben*, T.T.H. Arnesen, R.F. Peletier, M. Vecchi, C. Vlasakidis, M.H.F. Wilkinson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

38 Downloads (Pure)

Abstract

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera images of such events to differentiate the signal from the background and to reconstruct the energy of the initial gamma ray. Pattern spectra provide a 2-dimensional histogram of the sizes and shapes of features comprising an image and they can be used as an input for a CNN to significantly reduce the computational power required to train it. In this work, we generate pattern spectra from simulated gamma-ray and proton images to train a CNN for signal-background separation and energy reconstruction for the Small-Sized Telescopes (SSTs) of the Cherenkov Telescope Array (CTA). A comparison of our results with a CNN directly trained on CTA images shows that the pattern spectra-based analysis is about a factor of three less computationally expensive but not able to compete with the performance of an CTA image-based analysis. Thus, we conclude that the CTA images must be comprised of additional information not represented by the pattern spectra.
Original languageEnglish
Article number168942
Number of pages10
JournalNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume1059
Early online date18-Nov-2023
DOIs
Publication statusPublished - Feb-2024

Keywords

  • CTA
  • Gamma rays
  • Atmospheric shower reconstruction
  • Machine learning

Fingerprint

Dive into the research topics of 'Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array'. Together they form a unique fingerprint.

Cite this