Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics

Abstract

The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can "read" features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.

References in corpus (4)

• Learning phase transitions by confusion
• Theory of high harmonic generation in relativistic laser interaction with overdense plasma
• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
• Anomalies in Universal Intensity Scaling in Ultrarelativistic Laser-Plasma Interactions