Towards ML-Based Diagnostics of Laser-Plasma Interactions.

The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, the differences between the two can pose an obstacle to reliable data extraction. Here we report on the development of ML-based diagnostics for experiments on high-intensity laser-matter interactions.

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy.

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments.

Extreme plasma states in laser-governed vacuum breakdown.

Triggering vacuum breakdown at laser facility is expected to provide rapid electron-positron pair production for studies in laboratory astrophysics and fundamental physics. However, the density of the produced plasma may cease to increase at a relativistic critical density, when the plasma becomes opaque. Here, we identify the opportunity of breaking this limit using optimal beam configuration of petawatt-class lasers.