Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data

Authors

Ronak Desai, The Ohio State University
Thomas Zhang, The Ohio State University
J. J. Felice, The Ohio State University
Ricky Oropeza, The Ohio State University
Joseph R. Smith, Marietta College
Alona Kryshchenko, California State University, Channel Islands
Chris Orban, The Ohio State University
Michael L. Dexter, Air Force Institute of TechnologyFollow
Anil K. Patnaik, Air Force Institute of TechnologyFollow

Document Type

Article

Publication Date

11-22-2024

Abstract

In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine-learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine-learning model we considered, support vector regression performed very well in our tests.

Comments

© 2024 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.

The article was published as an article of Contributions to Plasma Physics ahead of inclusion in an issue.

Funding notes: This work was supported by the National Science Foundation (2109222), U.S. Department of Energy (89243021SSC000084), and Air Force Office of Scientific Research (23AFCOR004).

The data that support the findings of this study are openly available in Zenodo: 12752264.

DOI

10.1002/ctpp.202400080; Data: 10.5281/zenodo.12752264

Source Publication

Contributions to Plasma Physics (ISSN 0863-1042 | e-ISSN 1521-3986)

Recommended Citation

Desai, R., Zhang, T., Felice, J. J., Oropeza, R., Smith, J. R., Kryshchenko, A., Orban, C., Dexter, M. L., & Patnaik, A. K. (2024). Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data. Contributions to Plasma Physics, e202400080. https://doi.org/10.1002/ctpp.202400080