Work

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

Year: 2025

Type: conference-paper

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. 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.

Source: International Cosmic Ray Conference

DOI: 10.22323/1.501.0382

Authors: Pranav Sampathkumar, Tim Huege, Andreas Haungs, Ralph Engel

Institutions: Karlsruhe Institute of Technology, Vrije Universiteit Brussel

Language: English

Topic: Astrophysics and Cosmic Phenomena

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4414451533

FWCI:

Cites:

Cited by:

Related to: