Neural Network for Simulating Radio Emission from Extensive Air Showers

2025/12/24 by Pranav Sampathkumar, Sampathkumar, Pranav, Tim Huege +5
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #Dark Matter and Cosmic Phenomena #FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Neutrino Physics Research

paper · doi:10.48550/arxiv.2512.21407

openalex publication_date 2025/12/24 · openalex created_date 2025/12/30 · openalex updated_date 2026/07/28

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_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/.

Citations

• SMIET: Fast and accurate synthesis of radio pulses from extensive air shower using simulated templates
• Uncertainties in the Estimation of Air Shower Observables from Monte Carlo Simulation of Radio Emission
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
• Radio Measurements of the Depth of Air-Shower Maximum at the Pierre Auger Observatory
• Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

Related

• Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
• Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays
• Reconstruction of the depth of the shower maximum of air showers with the SD-750 surface detector of the Pierre Auger Observatory using neural networks
• Identification and Denoising of Radio Signals from Cosmic-Ray Air Showers using Convolutional Neural Networks
• Denoising radio pulses from air showers using machine-learning methods