Data for "Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas"

Separated training, validation, and test data from a set of magnetohydrodynamic turbulent box simulations, including a relativistic cosmic ray fluid spanning a range of cosmic ray transport modes: no cosmic rays (MHD), cosmic ray advection (CR_Advect), fiducial cosmic ray diffusion (CR_Diff_Fiducial), fiducial diffusion x 100 (CR_Diff100), and diffusion plus streaming (CR_withStreaming). Note this dataset comprises the "Full Power" images from Bustard and Wu 2024, submitted to Machine Learning Science and Technology. Scripts to analyze this data are available at https://github.com/bustardchad/ML_Turb. Paper abstract: "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."