  A variety of spherical crystal x-ray imager (SCXI) diagnostics have been
developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments
at the Sandia National Laboratories Z-facility. These different imaging
modalities provide detailed insight into different physical phenomena such as
mix of liner material into the hot fuel, cold liner emission, or reduce impact
of liner opacity. However, several practical considerations ranging from the
lack of a consistent spatial fiducial for registration to different
point-spread-functions and tuning crystals or using filters to highlight
specific spectral regions make it difficult to develop broadly applicable
metrics to compare experiments across our stagnation image database without
making significant unverified assumptions. We leverage experimental data for a
model-free assessment of sensitivities to instrumentation-based features for
any specified image metric. In particular, we utilize a database of historical
and recent MagLIF data including $N_{\text{scans}} = 139$ image plate scans
gathered across $N_{\text{exp}} = 67$ different experiments to assess the
impact of a variety of features in the experimental observations arising from
uncertainties in registration as well as discrepancies in signal-to-noise ratio
and instrument resolution. We choose a wavelet-based image metric known as the
Mallat Scattering Transform for the study and highlight how alternate metric
choices could also be studied. In particular, we demonstrate a capability to
understand and mitigate the impact of signal-to-noise, image registration, and
resolution difference between images. This is achieved by utilizing multiple
scans of the same image plate, sampling random translations and rotations, and
applying instrument specific point-spread-functions found by ray tracing to
high-resolution datasets, augmenting our data in an effectively model-free
fashion.

Plasma Physics;Data Analysis, Statistics and Probability