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

TL;DR

This paper demonstrates how machine learning, specifically neural networks, can validate theories and identify experimental conditions in laser-plasma physics by analyzing spectral data, overcoming limitations of traditional methods.

Contribution

It introduces a machine learning approach to interpret laser-plasma spectra, enabling theory validation and experimental condition identification without complete prior knowledge.

Findings

Neural networks can interpret laser-plasma harmonic spectra effectively.

Machine learning overcomes limitations of ab-initio simulations.

The approach aids in resolving theoretical and experimental challenges.

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.