Neural network sampling of Bethe-Heitler process in particle-in-cell codes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910472082554880 |
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| author | Amaro, Óscar Badiali, Chiara Martinez, Bertrand |
| author_facet | Amaro, Óscar Badiali, Chiara Martinez, Bertrand |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural network sampling of Bethe-Heitler process in particle-in-cell codes Amaro, Óscar Badiali, Chiara Martinez, Bertrand Computational Physics Plasma Physics 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. |
| title | Neural network sampling of Bethe-Heitler process in particle-in-cell codes |
| topic | Computational Physics Plasma Physics |
| url | https://arxiv.org/abs/2406.02491 |