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Classification and Denoising of Cosmic-Ray Radio Signals using Deep Learning

Rehman, Abdul; Coleman, Alan; Schröder, Frank G.; Kostunin, Dmitriy


The radio detection technique, with advantages like inexpensive detector hardware and full year duty cycle, can prove to be a vital player in cosmic-ray detection at the highest energies and can lead us to the discovery of high energy particle accelerators in the universe. However, radio detection has to deal with continuous, irreducible background. The Galactic and thermal backgrounds, which contaminate the radio signal from air showers, lead to a relatively high detection threshold compared to other techniques. For the purpose of reducing the background, we employ a deep learning technique namely, convolutional neural networks (CNN). This technique has already proven to be efficient for radio pulse recognition e.g., in the Tunka-Rex experiment. We train CNNs on the radio signal and background to separate both from each other. The goal is to improve the radio detection threshold on the one hand, and on the other hand, increase the accuracy of the arrival time and amplitude of the radio pulses and consequently improve the reconstruction of the primary cosmic-ray properties. Here we present two different networks: a Classifier, which can be used to distinguish the radio signals from the pure background waveforms, and a Denoiser, which allows us to mitigate the background from the noisy traces and hence recover the underlying radio signal.

Verlagsausgabe §
DOI: 10.5445/IR/1000138538
Veröffentlicht am 06.10.2021
DOI: 10.22323/1.395.0417
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Astroteilchenphysik (IAP)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 07.2021
Sprache Englisch
Identifikator ISSN: 1824-8039
KITopen-ID: 1000138538
HGF-Programm 51.13.04 (POF IV, LK 01) Kosmische Strahlung Technologien
Erschienen in 37th International Cosmic Ray Conference (ICRC 2021): July 12th – 23rd, 2021, Online – Berlin, Germany
Veranstaltung 37th International Cosmic Ray Conference (ICRC 2021), Online, 12.07.2021 – 23.07.2021
Verlag Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Seiten Art.-Nr.: 417
Serie Proceedings of Science ; 395
Nachgewiesen in Dimensions
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