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arXiv · 2406.02491

Neural network sampling of Bethe-Heitler process in particle-in-cell codes

Óscar Amaro·Chiara Badiali·Bertrand Martinez
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Abstract

This study uses neural networks to improve Monte Carlo (MC) implementations of the Bethe-Heitler process in Particle-In-Cell (PIC) codes. We provide a neural network that is as accurate as pre-calculated tables, and requires a hundred times less memory to store. It is trained to predict Bethe-Heitler pair production cross-sections for atomic numbers 1-50 and photon energies between 1 MeV and 10 GeV in the PIC code OSIRIS. We first validate our approach against a theoretical estimate in a simplified context. We later prove that both approaches have similar performance in a typical relativistic laser-plasma interaction scenario. The large memory decrease accessible with neural networks will enable introducing more advanced cross-section models for Bethe-Heitler pair production and other QED mechanisms in the MC modules of PIC codes.

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Óscar Amaro, Chiara Badiali, Bertrand Martinez. 2024-06-04. Neural network sampling of Bethe-Heitler process in particle-in-cell codes. https://arxiv.org/abs/2406.02491

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