Speaker
Description
With over 60,000 antennas deployed within a square kilometre radius, the high antenna density of the low-frequency counterpart of the Square Kilometre Array (SKA-Low) will not only perform cosmic ray observations with unprecedented accuracy, but also has the potential to reconstruct parameters beyond the current state-of-the-art. In this work, we develop a framework to reconstruct the longitudinal profile of cosmic ray air showers using measurements from dense radio antenna arrays, in particular with SKA-Low. Our model can incorporate explicit prior knowledge about our current physical understanding of air shower physics, utilises SMIET, a fast-forward simulation framework tailored to synthesise electric field pulses from any given profile, and also includes realistic antenna models to most accurately describe the observed voltage traces in each detector. The reconstruction relies on Information Field Theory (IFT), which utilises Bayesian inference to yield various realisations of the reconstructed profile from our model. IFT enables us to extract all available information in the signal (amplitude, phase, pulse shape, relative timing), and it does not explicitly rely on CoREAS simulations, drastically reducing the required computation time for reconstruction. Through this framework, the combined information of the reconstructed shower parameters will deepen our understanding of the particle interactions within these air showers, thus aiding us in a more accurate reconstruction of the mass composition of cosmic rays.