Event Reconstruction for Radio-Based In-Ice Neutrino Detectors with Neural Posterior Estimation

TL;DR

This paper introduces a neural network that reconstructs neutrino properties from radio waveforms, providing full posterior PDFs and improving accuracy over previous methods for in-ice UHE neutrino detection.

Contribution

It presents a novel deep learning approach using neural posterior estimation with normalizing flows for event reconstruction in neutrino detectors.

Findings

Achieves median energy resolution of 0.30 log(E) for shallow detectors and 0.08 log(E) for deep detectors.

Provides directional resolution of 18 to 28 square degrees depending on detector depth.

Successfully reconstructs stochastic $ u_e$ - charged current events.

Abstract

The detection of ultra-high-energy (UHE) neutrinos in the EeV range is the goal of current and future in-ice radio arrays at the South Pole and in Greenland. Here, we present a deep neural network that can reconstruct the main neutrino properties of interest from the raw waveforms recorded by the radio antennas: the neutrino direction, the energy of the particle shower induced by the neutrino interaction, and the event topology, thereby estimating the neutrino flavor. For the first time, we predict the full posterior PDF for the energy and direction reconstruction via neural posterior estimation utilizing conditional normalizing flows, enabling event-by-event uncertainty prediction. We improve over previous reconstruction algorithms and obtain a median resolution of 0.30 log(E) and 18 square degrees for a 'shallow' detector component and 0.08 log(E) and 28 square degrees for a 'deep' detector component for neutral current (NC) events at a shower energy of 1 EeV. This deep learning approach also allows us to reconstruct the more stochastic $\nu_e$ - charged current (CC) events. We quantify the impact of different antenna types and systematic uncertainties on the reconstruction and derive a goodness-of-fit score to test the compatibility of measured neutrino signals with the Monte Carlo simulations used to train the neural network.