Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915199882100736 |
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| author | Sytov, A. Cassou, K. Kubytskyi, V. Lenivenko, M. Huber, A. |
| author_facet | Sytov, A. Cassou, K. Kubytskyi, V. Lenivenko, M. Huber, A. |
| contents | We present the development and integration of a Machine Learning (ML)-based surrogate model, trained on Particle-In-Cell (PIC) simulations of laser-driven plasma wakefield acceleration source of electrons, into Geant4 simulation toolkit. Our model enables the generation and tracking of plasma-accelerated beams within complete experimental setups, unifying plasma acceleration and Monte Carlo-based simulations, which significantly reduces their complexity and computational cost.
Our implementation focuses on the PALLAS laser-plasma accelerator test facility, integrating its full experimental setup into Geant4. We describe the ML model, its integration into Geant4, and key simulation results, demonstrating the feasibility of start-to-end simulations of plasma acceleration facilities and applications within a unified framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment Sytov, A. Cassou, K. Kubytskyi, V. Lenivenko, M. Huber, A. Plasma Physics Accelerator Physics We present the development and integration of a Machine Learning (ML)-based surrogate model, trained on Particle-In-Cell (PIC) simulations of laser-driven plasma wakefield acceleration source of electrons, into Geant4 simulation toolkit. Our model enables the generation and tracking of plasma-accelerated beams within complete experimental setups, unifying plasma acceleration and Monte Carlo-based simulations, which significantly reduces their complexity and computational cost. Our implementation focuses on the PALLAS laser-plasma accelerator test facility, integrating its full experimental setup into Geant4. We describe the ML model, its integration into Geant4, and key simulation results, demonstrating the feasibility of start-to-end simulations of plasma acceleration facilities and applications within a unified framework. |
| title | Integration of Machine Learning-Based Plasma Acceleration Simulations into Geant4: A Case Study with the PALLAS Experiment |
| topic | Plasma Physics Accelerator Physics |
| url | https://arxiv.org/abs/2503.12154 |