  The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally
constrained by phenomenological models of Milky Way CR propagation fit to a
variety of direct and indirect observables; however, constraining the
fine-grained transport of CRs along individual magnetic field lines -- for
instance, diffusive vs streaming transport models -- is an unsolved challenge.
Leveraging a recent training set of magnetohydrodynamic turbulent box
simulations, with CRs spanning a range of transport parameters, we use
convolutional neural networks (CNNs) trained solely on gas density maps to
classify CR transport regimes. We find that even relatively simple CNNs can
quite effectively classify density slices to corresponding CR transport
parameters, distinguishing between streaming and diffusive transport, as well
as magnitude of diffusivity, with class accuracies between $92\%$ and $99\%$.
As we show, the transport-dependent imprints that CRs leave on the gas are not
all tied to the resulting density power spectra: classification accuracies are
still high even when image spectra are flattened ($85\%$ to $98\%$ accuracy),
highlighting CR transport-dependent changes to turbulent phase information. We
interpret our results with saliency maps and image modifications, and we
discuss physical insights and future applications.
