Classification of Magnetohydrodynamic Simulations using Wavelet
  Scattering Transforms
  The complex interplay of magnetohydrodynamics, gravity, and supersonic
turbulence in the interstellar medium (ISM) introduces non-Gaussian structure
that can complicate comparison between theory and observation. We show that the
Wavelet Scattering Transform (WST), in combination with linear discriminant
analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps.
WST-LDA classifies magnetohydrodynamic (MHD) turbulence simulations with up to
a 97\% true positive rate in our testbed of 8 simulations with varying sonic
and Alfv\'{e}nic Mach numbers. We present a side-by-side comparison with two
other methods for non-Gaussian characterization, the Reduced Wavelet Scattering
Transform (RWST) and the 3-Point Correlation Function (3PCF). We also
demonstrate the 3D-WST-LDA and apply it to classification of density fields in
position-position-velocity (PPV) space, where density correlations can be
studied using velocity coherence as a proxy. WST-LDA is robust to common
observational artifacts, such as striping and missing data, while also
sensitive enough to extract the net magnetic field direction for
sub-Alfv\'{e}nic turbulent density fields. We include a brief analysis of the
effect of point spread functions and image pixelization on 2D-WST-LDA applied
to density fields, which informs the future goal of applying WST-LDA to 2D or
3D all-sky dust maps to extract hydrodynamic parameters of interest.
