Information Field Theory for Two Applications in Astroparticle Physics - Martin Erdmann; Frederik Krieger; Alex Reuzki; Josina Schulte; Michael Smolka; Maximilian Straub (Particles, 2025)
Information field theory (IFT) provides a powerful framework for reconstructing continuous fields from noisy and sparse data. Based on Bayesian statistics, IFT allows for the approximation of posterior distributions over field-like parameter spaces in high-dimensional problems. In this contribution, we discuss two applications of IFT in the context of astroparticle physics. First, we present its intended use for the calibration of the newly installed radio detector upgrade of the Pierre Auger Observatory. Second, we demonstrate its application to infer the initial directions of ultra-high-energy cosmic rays before their deflection in the Galactic magnetic field using a simplified model.