Neural Network for Simulating Radio Emission from Extensive Air Showers

TL;DR

This paper demonstrates that neural networks can efficiently simulate radio emissions from cosmic ray air showers, enabling faster analysis and accurate $X_{max}$ reconstruction comparable to traditional Monte-Carlo methods.

Contribution

It introduces a neural network approach for simulating radio pulses from air showers, reducing computational costs while maintaining accuracy.

Findings

Neural networks can replicate radio emission simulations effectively.

The approach achieves comparable $X_{max}$ resolution to full simulations.

Code implementation is publicly available.

Abstract

Cosmic ray shower detection using large radio arrays has gained significant traction in recent years. With massive improvements in signal modelling and microscopic simulations, the analysis of incoming events is still severely limited by the simulation cost of radio emission to interpret the data. In this work, we show that a neural network can be used for simulating such radio pulses. This work serves as a proof of concept that simple neural networks can be used for emergent deterministic macroscopic phenomena of microscopic simulations. We also demonstrate how such a neural network can be used for the physics use case of $X_\mathrm{max}$ reconstruction, while retaining comparable resolution to using full Monte-Carlo simulations for radio emission. Code available at https://anonymous.4open.science/r/radio_nn-21BF/.