Information Field Theory with JAX infers Air Shower Electric Currents from Antenna Signal Traces

TL;DR

This paper introduces a novel method combining Bayesian inference and JAX to reconstruct and image cosmic-ray-induced air showers from antenna data, advancing observational capabilities in astroparticle physics.

Contribution

It presents a new approach integrating information field theory with JAX for detailed imaging of air showers from antenna signals, a novel application in the field.

Findings

Successfully reconstructs particle shower trajectories from antenna data

Demonstrates the feasibility of detailed imaging of cosmic-ray showers

Provides a pathway for improved understanding of high-energy astrophysical phenomena

Abstract

Direct imaging of cosmic-ray-induced particle showers during daylight is a long-standing challenge in astroparticle physics. A promising avenue for capturing images of these showers is through the radio emissions generated by their electrically charged particles. Their corresponding current vectors evolve over time as the particle shower propagates through the Earth's atmosphere leading to a characteristic time-dependent electric field in an antenna array. In this work, we harness modern Bayesian inference techniques within the Python toolkit for numerical information field theory NIFTy, coupled with the high-performance numerical computing capabilities of the Python library JAX. This innovative combination enables us to reconstruct the particle shower and its temporal development from data collected by a ground-based antenna array. Our approach opens an initial pathway for detailed imaging of cosmic-ray showers, potentially advancing our understanding of high-energy astrophysical processes.