Modeling of a Liquid Leaf Target TNSA Experiment using Particle-In-Cell Simulations and Deep Learning

TL;DR

This paper develops a deep learning surrogate model trained on PIC simulation data to optimize liquid leaf target laser-ion acceleration experiments, enabling rapid parameter tuning and system insights.

Contribution

It introduces a neural network-based surrogate model for liquid leaf TNSA experiments, facilitating fast optimization and analysis of laser-ion acceleration parameters.

Findings

Deep learning model achieves fast inference for experiment optimization.

Parameter analysis reveals key factors influencing ion energy and efficiency.

Model provides insights into laser-plasma interactions using Sobol and PAWN indices.

Abstract

Liquid leaf targets show promise as high repetition rate targets for laser-based ion acceleration using the Target Normal Sheath Acceleration (TNSA) mechanism and are currently under development. In this work, we discuss the effects of different ion species and investigate how they can be leveraged for use as a possible laser-driven neutron source. To aid in this research, we develop a surrogate model for liquid leaf target laser-ion acceleration experiments, based on artificial neural networks. The model is trained using data from Particle-In-Cell (PIC) simulations. The fast inference speed of our deep learning model allows us to optimize experimental parameters for maximum ion energy and laser-energy conversion efficiency. An analysis of parameter influence on our model output, using Sobol and PAWN indices, provides deeper insights into the laser-plasma system.