A novel approach for air shower profile reconstruction using radio measurements with Information Field Theory

Reconstructing the profile of extensive air showers, generated from the interaction of cosmic rays in the Earth's atmosphere, is crucial to understanding their mass composition, which in turn provides valuable insight on their possible source of origin. However, current frameworks can only recover shower parameters that provide limited information on the composition and relies on computationally expensive simulations. In this work, we develop a novel framework to reconstruct the longitudinal profile of air showers using measurements from radio detectors with Information Field Theory, a state-of-the-art reconstruction framework based on Bayesian inference. We utilise prior knowledge about the physical process of radio emission to generate a fast-forward model based on template synthesis and incorporate realistic response and noise models to produce voltage traces at each antenna. We apply our framework with simulated datasets based on the LOFAR detector layout and analyse the reconstruction efficiency to highlight the performance of our framework.