Bayesian analysis of cosmic-ray propagation: evidence against
  homogeneous diffusion
  We present the results of the most complete ever scan of the parameter space
for cosmic ray (CR) injection and propagation. We perform a Bayesian search of
the main GALPROP parameters, using the MultiNest nested sampling algorithm,
augmented by the BAMBI neural network machine learning package. This is the
first such study to separate out low-mass isotopes ($p$, $\bar p$ and He) from
the usual light elements (Be, B, C, N, O). We find that the propagation
parameters that best fit $p$, $\bar p$, He data are significantly different
from those that fit light elements, including the B/C and $^{10}$Be/$^9$Be
secondary-to-primary ratios normally used to calibrate propagation parameters.
This suggests each set of species is probing a very different interstellar
medium, and that the standard approach of calibrating propagation parameters
using B/C can lead to incorrect results. We present posterior distributions and
best fit parameters for propagation of both sets of nuclei, as well as for the
injection abundances of elements from H to Si. The input GALDEF files with
these new parameters will be included in an upcoming public GALPROP update.
