Likelihood Reconstruction for Radio Detectors of Neutrinos and Cosmic Rays

TL;DR

This paper introduces a likelihood-based method for reconstructing neutrino and cosmic-ray signals in radio detectors, accounting for correlated noise to improve accuracy, uncertainty estimation, and detector optimization.

Contribution

It presents a novel likelihood framework that incorporates noise correlations for radio detector signal reconstruction, enhancing precision and enabling event-by-event uncertainty estimation.

Findings

Improved reconstruction accuracy for energy and direction.

Event-by-event uncertainties with correct coverage.

Reduced biases compared to previous methods.

Abstract

Ultra-high-energy neutrinos and cosmic rays are excellent probes of astroparticle physics phenomena. For astroparticle physics analyses, robust and accurate reconstruction of signal parameters such as arrival direction and energy is essential. Radio detection is an established detector concept explored by many observatories; however, current reconstruction methods ignore bin-to-bin noise correlations, which limits reconstruction resolution and, so far, has prevented calculations of event-by-event uncertainties. In this work, we present a likelihood description of neutrino or cosmic-ray signals in radio detectors with correlated noise, as present in all neutrino and cosmic-ray radio detectors. We demonstrate, with simulation studies of both neutrinos and cosmic-ray radio signals, that signal parameters such as energy and direction, including event-by-event uncertainties with correct coverage, can be obtained. This method reduces reconstruction uncertainties and biases compared to previous approaches. Additionally, the Likelihood can be used for event selection and enables differentiable end-to-end detector optimization. The reconstruction code is available through the open-source software NuRadioReco.