Speaker
Description
Extensive air showers (EAS), created from interactions of highly energetic cosmic particles with the atmosphere, produce radio emission via the geomagnetic and Askaryan effect. These radio signals provide valuable information about the properties of the primary particle that started the air shower. Dense radio detector arrays such as LOFAR and the future SKA-low allow high-precision reconstruction of shower parameters such as the depth of the shower maximum. However, current reconstruction methods rely heavily on simulations and hadronic interaction models. Thus, a model-agnostic reconstruction approach is of great interest. Furthermore, current state-of-the-art reconstruction algorithms are able to reconstruct air showers in at most three dimensions. Additionally, the time evolution of the air shower may also provide crucial information. The goal of our project is therefore to develop a time-resolved 3D imaging method in order to reconstruct air showers in 4D. In order to tackle this endeavour, a vast abundance of high-precision data is required, which the future SKA-low array with its roughly 60000 antennas is expected to deliver. The reconstruction algorithm itself will employ Information Field Theory (IFT), a novel reconstruction framework based on Bayesian inference.
