Quantification of MagLIF morphology using the Mallat Scattering Transformation

TL;DR

This paper introduces a novel image morphology metric based on the Mallat Scattering Transformation (MST) to quantitatively compare and analyze the textures of plasma images from MagLIF experiments and simulations, enhancing diagnostic capabilities.

Contribution

The authors developed and validated a new MST-based metric for classifying and comparing plasma image textures, linking the transformation to physical system dynamics from kinetic and field theory perspectives.

Findings

MST effectively classifies synthetic stagnation images.

The metric enables quantitative comparison between experimental and simulation images.

A GPU-accelerated Python implementation of MST was created.

Abstract

The morphology of the stagnated plasma resulting from Magnetized Liner Inertial Fusion (MagLIF) is measured by imaging the self-emission x-rays coming from the multi-keV plasma, and the evolution of the imploding liner is measured by radiographs. Equivalent diagnostic response can be derived from integrated rad-MHD simulations from programs such as Hydra and Gorgon. There have been only limited quantitative ways to compare the image morphology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat Scattering Transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric has demonstrated excellent performance in classifying ensembles of synthetic stagnation images. We use this metric to quantitatively compare simulations to experimental images, cross experimental images, and to estimate the parameters of the images with uncertainty via a linear regression of the synthetic images to the parameters used to generate them. This coordinate space has proved very adept at doing a sophisticated relative background subtraction in the MST space. This was needed to compare the experimental self emission images to the rad-MHD simulation images. We have also developed theory that connects the transformation to the causal dynamics of physical systems. This has been done from the classical kinetic perspective and from the field theory perspective, where the MST is the generalized Green's function, or S-matrix of the field theory in the scale basis. From both perspectives the first order MST is the current state of the system, and the second order MST are the transition rates from one state to another. An efficient, GPU accelerated, Python implementation of the MST was developed. Future applications are discussed.