Reconstructing non-repeating radio pulses with Information Field Theory

TL;DR

This paper applies Information Field Theory, a Bayesian inference method, to accurately reconstruct non-repeating radio pulses from particle showers in noisy environments, aiding cosmic ray and neutrino detection.

Contribution

It introduces a novel application of Information Field Theory for reconstructing radio signals from particle showers in air and ice, improving analysis accuracy.

Findings

Accurate pulse parameter reconstruction from noisy data.

Effective application of Bayesian inference to radio signals.

Enhanced detection capabilities for cosmic rays and neutrinos.

Abstract

Particle showers in dielectric media produce radio signals which are used for the detection of both ultra-high energy cosmic rays and neutrinos with energies above a few PeV. The amplitude, polarization, and spectrum of these short, broadband radio pulses allow us to draw conclusions about the primary particles that caused them, as well as the mechanics of shower development and radio emission. However, confidently reconstructing the radio signals can pose a challenge, as they are often obscured by background noise. Information Field Theory offers a robust approach to this challenge by using Bayesian inference to calculate the most likely radio signal, given the recorded data. In this paper, we describe the application of Information Field Theory to radio signals from particle showers in both air and ice and demonstrate how accurately pulse parameters can be obtained from noisy data.