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Developing New Analysis Tools for Near Surface Radio-based Neutrino Detectors by ARIANNA Collaboration; Anker, A.; Baldi, P.; Barwick, S. W.; Beise, J.; Besson, D. Z.; Chen, P.; Gaswint, G.; Glaser, C.; Hallgren, A.; Hanson, J. C.; Klein, S. R.; Kleinfelder, S. A.; Lahmann, R.; Liu, J.; Nam, J.; Nelles, A.; Paul, M. P.; Persichilli, C.; Plaisier, I.; Rice-Smith, R.; Tatar, J.; Terveer, K.; Wang, S. -H; Zhao, L. is a scholarly article available to read on EtoBox.

What is Developing New Analysis Tools for Near Surface Radio-based Neutrino Detectors about?

The ARIANNA experiment is an Askaryan radio detector designed to measure high-energy neutrino induced cascades within the Antarctic ice. Ultra-high-energy neutrinos above $10^{16}$ eV have an extremely low flux, so experimental data captured at trigger level need to be classified correctly to retain more neutrino signal. We first describe two new physics-based neutrino selection methods, (the updown and dipole cut) that extend the previously published analysis to a specialized ARIANNA station with 8 antenna channels, which is double the number used in the prior analysis. For a standard trigger with a threshold signal to noise ratio at 4.4, the new cuts produce a neutrino efficiency of > 95% per station-year, while rejecting 99.93% of the background (corresponding to 53 remaining experimental background events). When the new cuts are combined with a previously developed cut using neutrino waveform templates, all background is removed at no change of efficiency. In addition, the neutrino efficiency is extrapolated to 1,000 station-years, obtaining 91%. This work then introduces a new selection method (deep learning (DL) cut) to augment the identification of neutrino events by using D

Author

ARIANNA Collaboration; Anker, A.; Baldi, P.; Barwick, S. W.; Beise, J.; Besson, D. Z.; Chen, P.; Gaswint, G.; Glaser, C.; Hallgren, A.; Hanson, J. C.; Klein, S. R.; Kleinfelder, S. A.; Lahmann, R.; Liu, J.; Nam, J.; Nelles, A.; Paul, M. P.; Persichilli, C.; Plaisier, I.; Rice-Smith, R.; Tatar, J.; Terveer, K.; Wang, S. -H; Zhao, L.

Published

2023

Language

EN