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Title:Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data
Authors:ID Aschersleben, Jann (Author)
ID Bhattacharyya, Saptashwa (Author)
ID MARČUN, Barbara (Author)
ID Pérez Romero, Judit (Author)
ID Stanič, Samo (Author)
ID Vodeb, Veronika (Author)
ID Vorobiov, Serguei (Author)
ID Zaharijas, Gabrijela (Author)
ID Zavrtanik, Marko (Author)
ID Zavrtanik, Danilo (Author)
ID Živec, Miha (Author), et al.
Files:.pdf ICRC2021_697.pdf (1,24 MB)
MD5: 222319897107B317CDD5DDD3D7F93283
 
URL https://pos.sissa.it/395/
 
URL https://pos.sissa.it/395/697/pdf
 
Language:English
Work type:Not categorized
Typology:1.08 - Published Scientific Conference Contribution
Organization:UNG - University of Nova Gorica
Abstract: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, o˙ering 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 di˙erent 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.
Keywords:Cherenkov Telescope Array, very-high-energy astronomy, convolutional neural networks
Publication status:Published
Year of publishing:2021
PID:20.500.12556/RUNG-8421 New window
COBISS.SI-ID:164774659 New window
NUK URN:URN:SI:UNG:REP:OFRW7TZ8
Publication date in RUNG:18.09.2023
Views:1974
Downloads:5
Metadata:XML DC-XML DC-RDF
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ASCHERSLEBEN, Jann, BHATTACHARYYA, Saptashwa, MARČUN, Barbara, PÉREZ ROMERO, Judit, STANIČ, Samo, VODEB, Veronika, VOROBIOV, Serguei, ZAHARIJAS, Gabrijela, ZAVRTANIK, Marko, ZAVRTANIK, Danilo and ŽIVEC, Miha, 2021, Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data. In : 37th International Cosmic Ray Conference [online]. Published Scientific Conference Contribution. Trst, Italija. 2021. [Accessed 13 April 2025]. Retrieved from: https://repozitorij.ung.si/IzpisGradiva.php?lang=eng&id=8421
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Record is a part of a monograph

Title:37th International Cosmic Ray Conference : ICRC2021
Place of publishing:Trst, Italija
Year of publishing:2021

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License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
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