Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison

TL;DR

This paper benchmarks invertible neural networks (INNs) for analyzing electron spectra from laser-plasma interactions, demonstrating significantly improved acceptance rates and proposing a hybrid algorithm for efficient, accurate inference of plasma parameters.

Contribution

It provides a comprehensive benchmark of INNs against traditional methods for electron spectrum analysis and introduces a hybrid algorithm combining INNs for faster, accurate plasma diagnostics.

Findings

Acceptance rates increased up to a factor of 10 with INNs.

INNs offer significant speed-up over standard methods.

Proposed composite algorithm maintains high accuracy with low runtimes.

Abstract

While the interaction of ultra-intense ultra-short laser pulses with near- and overcritical plasmas cannot be directly observed, experimentally accessible quantities (observables) often only indirectly give information about the underlying plasma dynamics. Furthermore, the information provided by observables is incomplete, making the inverse problem highly ambiguous. Therefore, in order to infer plasma dynamics as well as experimental parameter, the full distribution over parameters given an observation needs to considered, requiring that models are flexible and account for the information lost in the forward process. Invertible Neural Networks (INNs) have been designed to efficiently model both the forward and inverse process, providing the full conditional posterior given a specific measurement. In this work, we benchmark INNs and standard statistical methods on synthetic electron spectra. First, we provide experimental results with respect to the acceptance rate, where our results show increases in acceptance rates up to a factor of 10. Additionally, we show that this increased acceptance rate also results in an increased speed-up for INNs to the same extent. Lastly, we propose a composite algorithm that utilizes INNs and promises low runtimes while preserving high accuracy.