Meta-Analysis of Laser Accelerated Ion Experiments using Machine Learning Methods

Laser ion acceleration in plasma physics is a rapidly advancing field with applications in material processing and proton therapy. Forecasting maximum proton energy remains challenging due to nonlinear interactions depending on multiple laser parameters and target characteristics. In this study, we compare traditional empirical models with contemporary data-driven methodologies, including linear regression, k-means clustering, neural networks, and random forests. We analyzed over 1,000 data points from 64 research articles, utilizing correlation matrices to identify key parameter dependencies and k-means clustering to discover distinct experimental regimes. Initial predictions were produced using the Fuchs et al. (2006) model, and data-driven methods including neural networks and random forests demonstrated improved predictive accuracy. Our results indicate that machine learning methodologies hold significant potential for optimizing beam characteristics and guiding research efforts, ultimately leading to more efficient use of experimental time and resources. We advocate for continued integration of data-driven approaches tailored specifically for high-energy density physics applications.