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, Xmax. This work provides not only point estimates but also a rigorous quantification of uncertainties. We achieve a resolution in Xmax of 25g/cm2 and a radiation energy resolution of 12% on simulations for LOFAR.
@article{arxiv.2602.19864,
title = {A Bayesian Method for Air-Shower Reconstruction using Information Field Theory},
author = {Karen Terveer and Sjoerd Bouma and Stijn Buitink and Arthur Corstanje and Vital De Henau and Vincent Eberle and Torsten A. Enßlin and Philipp Frank and Tim Huege and Philipp Laub and Katharine Mulrey and Anna Nelles and Simon Strähnz and Satyendra Thoudam and Keito Watanabe},
journal= {arXiv preprint arXiv:2602.19864},
year = {2026}
}