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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array

Aschersleben, J. ; Carlile, C. LU ; Dravins, D. LU orcid and Zmija, A. (2024)
Abstract
The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov 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 camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features... (More)
The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov 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 camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s) (Less)
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Contribution to conference
publication status
published
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keywords
Airborne telescopes, Cerenkov counters, Computerized tomography, Cosmic ray detectors, Cosmic rays, Feature Selection, Image analysis, Image reconstruction, Optical telescopes, Photons, Array images, Cherenkov emissions, Cherenkov telescope arrays, Convolutional neural network, Event reconstruction, Gamma-rays, Ground based, Ground level, Imaging atmospheric Cherenkov telescopes, Pattern spectrum, Cosmology
external identifiers
  • scopus:85213027066
DOI
10.22323/1.417.0211
language
English
LU publication?
yes
id
9576b72d-bb43-44b6-b88b-3cbdce608a0c
date added to LUP
2025-12-01 11:01:31
date last changed
2025-12-02 08:40:35
@misc{9576b72d-bb43-44b6-b88b-3cbdce608a0c,
  abstract     = {{The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov 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 camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)}},
  author       = {{Aschersleben, J. and Carlile, C. and Dravins, D. and Zmija, A.}},
  keywords     = {{Airborne telescopes; Cerenkov counters; Computerized tomography; Cosmic ray detectors; Cosmic rays; Feature Selection; Image analysis; Image reconstruction; Optical telescopes; Photons; Array images; Cherenkov emissions; Cherenkov telescope arrays; Convolutional neural network; Event reconstruction; Gamma-rays; Ground based; Ground level; Imaging atmospheric Cherenkov telescopes; Pattern spectrum; Cosmology}},
  language     = {{eng}},
  title        = {{Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array}},
  url          = {{http://dx.doi.org/10.22323/1.417.0211}},
  doi          = {{10.22323/1.417.0211}},
  year         = {{2024}},
}