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

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.