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Communication Dans Un Congrès Année : 2023

A neural network to classify GRAND radio time traces

Résumé

GRAND is a Ultra High Energy (UHE) cosmic particles detection project, consisting in a giant, self-triggered, antenna array. Wherever we decide to setup the antennas apart from polar areas, we will generally face a high rate of background signals, orders of magnitude higher than the rate of extensive air showers. To avoid the saturation of the acquisition, we need to reject a significant part of the data at the antenna level, with a more sophisticated method than a basic peak-over- baseline selection. We present here an attempt to discriminate air showers and background radio time traces with a convolutional neural network, using experimental data rather than simulations. These data were produced with TREND, a self-triggered 50-antennas array, which was the seed for the GRAND project. At the antenna-level, for a set of signals that had triggered TREND, it was possible to reject 82% of the ultra-dominant background, while preserving 86% of the air shower signals.
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Dates et versions

cea-04410294 , version 1 (22-01-2024)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Sandra Le Coz, Aurélien Benoit-Lévy, Olivier Martineau-Huynh. A neural network to classify GRAND radio time traces. ARENA2022 - 9th International workshop on Acoustic and Radio EeV Neutrino Detection Activites, Jun 2022, Saint-Jacques-de-Compostelle, Spain. pp.041, ⟨10.22323/1.424.0041⟩. ⟨cea-04410294⟩
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