Quantification of MagLIF morphology using the Mallat Scattering Transformation
This work addresses a domain-specific need in fusion research for better quantitative image analysis tools, representing an incremental improvement by applying an existing transformation to a new application area.
The paper tackled the problem of quantitatively comparing the morphology of stagnated plasma images in Magnetized Liner Inertial Fusion (MagLIF) between simulations and experiments, by developing a metric based on the Mallat Scattering Transformation (MST) that enables classification, regression, and parameter estimation with uncertainty.
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. Equivalent diagnostic response can be generated by integrated radiation-magnetohydrodynamic (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 is designed, demonstrated, and refined by classifying ensembles (i.e., classes) of synthetic stagnation images, and by regressing an ensemble of synthetic stagnation images to the morphology (i.e., model) parameters used to generate the synthetic images. We use this metric to quantitatively compare simulations to experimental images, experimental images to each other, and to estimate the morphological parameters of the experimental images with uncertainty. 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.