Document Type
Article
Publication Date
10-29-2025
Abstract
Advances in ultra‐intense laser technology have increased repetition rates and average power for chirped‐pulse laser systems, which offer a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser‐plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large ∼1.5 million data point synthetic data set for proton acceleration using a physics‐informed analytic model that we improved to include pre‐pulse physics. Then, we train different machine learning methods on this data set to determine which methods perform efficiently and accurately. Generally, we find that quasi‐real‐time training of neural network models using single‐shot data from a kHz repetition rate ultra‐intense laser system should typically be feasible on a single GPU. We also find that a less sophisticated model, like a polynomial regression, can be trained even faster and that the accuracy of these models is still good enough to be useful. We provide our source code and example synthetic data for others to test new machine learning methods and approaches to automated learning in this regime.
Source Publication
Contributions to Plasma Physics (ISSN 0863-1042 | eISSN 1521-3986)
Recommended Citation
Felice, J. J., Desai, R., Tamminga, N., Smith, J. R., Kryshchenko, A., Orban, C., Dexter, M. L., & Patnaik, A. K. (2025). Applying Machine Learning Methods to Laser Acceleration of Protons: Synthetic Data for Exploring the High Repetition Rate Regime. Contributions to Plasma Physics, e70049. https://doi.org/10.1002/ctpp.70049
Comments
© 2025 The Authors.
This article is published by Wiley, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Funding note: Funding: This work was supported by the National Science Foundation (2109222). Air Force Office of Scientific Research (23AFCOR004). U.S. Department of Energy (89243021SSC000084).
The data that support the findings of this study are openly available in Zenodo at https://zenodo.org/records/12752264, reference number 10.5281/zenodo.12752264.