Speaker
Description
Imaging cosmic-ray air showers via their radio emission is gaining renewed attention with the upcoming SKA-LOW, whose dense antenna arrays will measure radio emission from air showers with unprecedented resolution. To extract information about the shower structure and, ultimately, the properties of the primary cosmic ray, we develop an imaging algorithm using Bayesian inference within the framework of Information Field Theory, implemented with NIFTy. The air shower is modeled macroscopically as relativistically moving current distributions, without assuming a fixed parametric form, whose coherent radio emission produces the observed electric field traces at the antennas. We address the inverse problem of reconstructing these current distributions directly from the measured data. The reconstructed current profiles yield shower observables, the longitudinal profile of air showers, complementing the state-of-the-art reconstruction approaches being developed in parallel. We demonstrate reconstructions on synthetic data, compare results to the true current distributions, and aim to benchmark the method against the parametric macroscopic approach of MGMR3D. The algorithm is being designed to remain tractable for the large antenna multiplicities of SKA-LOW.