A novel trigger based on neural networks for radio neutrino detectors

Anker A, Paul MP, Baldi P, Barwick SW, Beise J, Bernhoff H, Besson DZ, Bingefors N, Cataldo M, Chen P, Fernández DG, Gaswint G, Glaser C, Hallgren A, Hallmann S, Hanson JC, Klein SR, Kleinfelder SA, Lahmann R, Liu J, Magnuson M, McAleer S, Meyers Z, Nam J, Nelles A, Novikov A, Persichilli C, Plaisier I, Pyras L, Rice-Smith R, Tatar J, Wang SH, Welling C, Zhao L (2022)

Publication Type: Conference contribution

Publication year: 2022


Publisher: Sissa Medialab Srl

Book Volume: 395

Conference Proceedings Title: Proceedings of Science

Event location: Virtual, Berlin, DEU


The ARIANNA experiment is a proposed Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies, the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The trigger thresholds are limited by the rate of triggering on unavoidable thermal noise fluctuations. The real-time thermal noise rejection algorithm enables the thresholds to be lowered substantially and increases the sensitivity by up to a factor of two compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove a high percentage of thermal events in real time while retaining most of the neutrino signals. We describe a CNN that runs on the current ARIANNA microcomputer and retains 95% of the neutrino signals at a thermal rejection factor of 105. Finally, the experimental verification from lab measurements are conducted.

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Anker, A., Paul, M.P., Baldi, P., Barwick, S.W., Beise, J., Bernhoff, H.,... Zhao, L. (2022). A novel trigger based on neural networks for radio neutrino detectors. In Proceedings of Science. Virtual, Berlin, DEU: Sissa Medialab Srl.


Anker, Astrid, et al. "A novel trigger based on neural networks for radio neutrino detectors." Proceedings of the 37th International Cosmic Ray Conference, ICRC 2021, Virtual, Berlin, DEU Sissa Medialab Srl, 2022.

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