Beyond optimization -- supervised learning applications in relativistic laser-plasma experiments

TL;DR

This paper demonstrates how machine learning can be used in relativistic laser-plasma experiments for predicting electron beam charge, analyzing laser wavefront aberrations, and improving data interpretation beyond mere optimization.

Contribution

It introduces supervised learning applications for data analysis, feature extraction, and robustness testing in relativistic laser-plasma experiments, extending ML use beyond optimization.

Findings

ML predicts electron beam charge from laser wavefront data.

Specific laser aberrations correlate with higher beam charges.

ML models are robust to virtual measurement errors.

Abstract

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. We also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.