A Bayesian Method for Air-Shower Reconstruction using Information Field Theory

TL;DR

This paper introduces a Bayesian inference framework using Information Field Theory for air-shower reconstruction from radio measurements, significantly improving efficiency and uncertainty quantification.

Contribution

It develops a fully differentiable physical model augmented with Gaussian processes, enabling fast and robust estimation of shower parameters from LOFAR data.

Findings

Achieves a $25 \, \mathrm{g/cm^2}$ resolution in $X_{\text{max}}$

Provides a $12\%$ resolution in radiation energy

Accelerates inference by three orders of magnitude compared to previous methods

Abstract

The radio detection of extensive air showers provides a powerful method for studying the origin of high-energy cosmic rays. The Low-Frequency Array (LOFAR) offers unprecedentedly detailed measurements of the radio emission footprint. However, fully exploiting this information requires advanced reconstruction techniques. In this paper, we introduce a novel framework for air shower reconstruction based on Bayesian inference and Information Field Theory (IFT). Our method is built on a fully differentiable forward model of the radio signal, which incorporates a physical emission parameterization and a precise wavefront model. Additionally, we augment this physical model with Gaussian processes to account for systematic uncertainties in both the signal fluence and arrival timing. By leveraging gradient information, our approach enables efficient (three orders of magnitude acceleration w.r.t.\ the legacy method) and robust inference of the underlying physical shower parameters, such as primary energy and the depth of shower maximum, $X_\text{max}$. This work provides not only point estimates but also a rigorous quantification of uncertainties. We achieve a resolution in $X_\text{max}$ of $25\,\mathrm{g/cm^2}$ and a radiation energy resolution of $12\%$ on simulations for LOFAR.