A Generative Neural Network for the Prediction of Radio Pulses from Extensive Air Showers

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. We also show how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining comparable resolution as using full Monte-Carlo CORSIKA/CoREAS simulations for radio emission.