Towards ML-Based Diagnostics of Laser-Plasma Interactions.

Area of Science:

• Physics
• Computer Science
• Data Science

Background:

• Machine learning (ML) offers powerful feature identification for quantifying experimental results.
• A key challenge is the discrepancy between simulated training data and real experimental data.
• This gap can hinder reliable data extraction in ML-driven scientific applications.

Purpose of the Study:

• To develop robust ML-based diagnostics for high-intensity laser-matter interaction experiments.
• To enhance ML model tolerance to differences between simulated and experimental data.
• To identify physics-governed features that are robust to data discrepancies.

Main Methods:

• Investigated principal component analysis (PCA) for feature extraction.
• Applied data augmentation techniques to simulated datasets.
• Trained ML models using synthetic data with superimposed noise of gradually increasing amplitude.

Main Results:

• The approach of training with gradually increasing noise amplitude yielded the most accurate ML models.
• This method demonstrated improved robustness against the simulation-experiment data gap.
• Principal component analysis and data augmentation were also evaluated.

Conclusions:

• Training ML models with increasing noise amplitude is a promising strategy for improving diagnostic accuracy in laser-matter experiments.
• This technique can mitigate challenges arising from discrepancies between simulated and real-world data.
• The developed ML diagnostics are likely applicable to similar scientific projects requiring robust data analysis.