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. However, the time evolution of the air shower may also provide crucial information. The goal of this project is therefore to develop a model-agnostic algorithm to reconstruct air showers in 4D (space + time). Using this method, we expect to reconstruct shower parameters with unprecedented precision and possibly alter our understanding of air shower development. In order to tackle this endeavor, a vast abundance of high-precision data is required, which the future SKA-low array with its dense core of 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.
| What is your career stage? | Graduate researcher (pre PhD) |
|---|---|
| Which telescopes do you use / are you affiliated with? | LOFAR, SKA |
