Reconstruction of Extensive Air Showers from Radio Detector Data using Information Field Theory

Using radio detectors for cosmic rays is a very appealing approach, as they are cost-effective, have a duty cycle of nearly 100% and can directly probe the electromagnetic component of extensive air showers. However, reconstructing the electric field from the measured voltages in an antenna by unfolding the antenna response comes with several challenges, mainly because of measurement noise. These issues could be solved by Bayesian inference. The challenge with that approach is that the electric field is continuous, which would lead to an infinite-dimensional latent space. Information field theory (IFT) has been developed to deal with this problem and allow for Bayesian reasoning on fields. We will present a signal model that can be used with IFT based inference algorithms that can successfully reconstruct the electric field measured by a single antenna. The performance of this method has been demonstrated with Monte Carlo simulations of air shower radio signals. We will also show extended models being developed to combine the data from all antennas in a given array and reconstruct entire events. Since Bayesian inference provides the posterior distribution, this method also provides an estimate of the uncertainty of the measured field.