Beyond optimization—supervised learning applications in relativistic laser-plasma experiments
Abstract
We explore the applications of a variety of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. With the trained supervised learning models, the beam charge of electrons produced in a laser wakefield accelerator is predicted given the laser wavefront change caused by a deformable mirror. Feature importance analysis using the trained models shows that specific aberrations in the laser wavefront are favored in generating higher beam charges, which reveals more information than the genetic algorithms and the statistical correlation do. The predictive models enable operations beyond merely searching for an optimal beam charge. The quality of the measured data is characterized, and anomaly detection is demonstrated. The model robustness against measurement errors is examined by applying a range of virtual measurement error bars to the experimental data. This work demonstrates a route to machine learning applications in a highly nonlinear problem of relativistic laser-plasma interaction for in-depth data analysis to assist physics interpretation.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2021
- Source ID
- 10.1063/5.0047940
Entities
People
- Abigail Hsu
- Alexander G. Thomas
- Alfred O. Hero III
- Jinpu Lin
- Jon Murphy
- Karl Krushelnick
- Qian Qian
- Yong Ma
Organizations
- Air Force Office of Scientific Research
- Stony Brook University
- United States Department of Energy
- University of Michigan