Generative Neural Network for Simulating Radio Emission from Extensive Air Showers

Fuente: arXiv
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Autores principales: Sampathkumar, Pranav, Huege, Tim, Haungs, Andreas, Engel, Ralph
Formato: Preprint
Publicado: 2025
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author Sampathkumar, Pranav
Huege, Tim
Haungs, Andreas
Engel, Ralph
author_facet Sampathkumar, Pranav
Huege, Tim
Haungs, Andreas
Engel, Ralph
contents 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 demonstrate how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining comparable resolution to using full Monte-Carlo CORSIKA/CoREAS simulations for radio emission.
format Preprint
id arxiv_https___arxiv_org_abs_2507_07713
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
Sampathkumar, Pranav
Huege, Tim
Haungs, Andreas
Engel, Ralph
High Energy Astrophysical Phenomena
Instrumentation and Methods for Astrophysics
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 demonstrate how such a neural network can be used for $X_\mathrm{max}$ reconstruction, while retaining comparable resolution to using full Monte-Carlo CORSIKA/CoREAS simulations for radio emission.
title Generative Neural Network for Simulating Radio Emission from Extensive Air Showers
topic High Energy Astrophysical Phenomena
Instrumentation and Methods for Astrophysics
url https://arxiv.org/abs/2507.07713