Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors
C. Glaser, S. McAleer, S. Stj\"arnholm, P. Baldi, S. W. Barwick

TL;DR
This paper demonstrates that deep neural networks can accurately reconstruct the energy and direction of ultra-high-energy neutrinos using in-ice radio detector data, achieving resolutions suitable for scientific goals.
Contribution
It introduces a novel end-to-end deep learning approach for neutrino reconstruction, including for complex electron neutrino interactions, on simulated in-ice radio detection data.
Findings
Energy resolution with a standard deviation of about a factor of two.
Angular resolution around 1 degree for some events, with a 68% quantile of 4-5 degrees.
Effective modeling of complex correlations in radio detector signals.
Abstract
Ultra-high-energy (UHE) neutrinos ( eV) can be measured cost-effectively using in-ice radio detection, which has been explored successfully in pilot arrays. A large radio detector is currently being constructed in Greenland with the potential to measure the first UHE neutrino, and an order-of-magnitude more sensitive detector is being planned with IceCube-Gen2. For such shallow radio detector stations, we present an end-to-end reconstruction of the neutrino energy and direction using deep neural networks (DNNs) developed and tested on simulated data. The DNN determines the energy with a standard deviation of a factor of two around the true energy ( 0.3 in ), which meets the science requirements of UHE neutrino detectors. For the first time, we are able to predict the neutrino direction precisely for all event topologies including the complicated…
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Neutrino Physics Research · Radio Astronomy Observations and Technology
