Deep learning reconstruction of the neutrino energy with a shallow Askaryan detector

• Published in: Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)
• Main Authors: Glaser, Christian, McAleer, Stephen, Baldi, Pierre, Barwick, Steven
• Format: Conference Object
• Language: English
• Published: Uppsala universitet, Högenergifysik 2022
• Subjects: Subatomic Physics; Subatomär fysik; Accelerator Physics and Instrumentation; Acceleratorfysik och instrumentering; Greenland
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-518306; https://doi.org/10.22323/1.395.1051

Cost effective in-ice radio detection of neutrinos above a few 10(16) eV has been explored successfully in pilot-arrays. Alarge radio detector is currently being constructed in Greenland with the potential to measure the first cosmogenic neutrino, and an order-of-magnitude more sensitive detector is being planned with IceCube-Gen2. We present the first end-to-end reconstruction of the neutrino energy from radio detector data. NuRadioMC was used to create a large data set of 40 million events of expected radio signals that are generated via the Askaryan effect following a neutrino interaction in the ice for a broad range of neutrino energies between 100 PeV and 10 EeV. We simulated the voltage traces that would be measured by the five antennas of a shallow detector station in the presence of noise. We designed and trained a deep neural network to determine the shower energy directly from the simulated experimental data and achieve a resolution better than a factor of two (STD < 0.3 in log10(E)) which is below the irreducible uncertainty from inelasticity fluctuations. We present the model architecture and study the dependence of the resolution on event parameters. This method will enable Askaryan detectors to measure the neutrino energy.