%0 Journal Article
%T Bayesian analysis of cosmic-ray propagation: evidence against homogeneous diffusion
%A Johannesson, G.
%A Ruiz de Austri, R.
%A Vincent, A. C.
%A Moskalenko, I. V.
%A Orlando, E.
%A Porter, T. A.
%A Strong, A. W.
%A Trotta, R.
%A Feroz, F.
%A Graff, P.
%A Hobson, M. P.
%J Astrophysical Journal
%D 2016
%V 824
%N 1
%I Iop Publishing Ltd
%@ 0004-637x
%G English
%F Johannesson_etal2016
%O WOS:000377937300016
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=2727), last updated on Thu, 07 Jul 2016 11:10:05 +0000
%X We present the results of the most complete 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 study to separate out low-mass isotopes (p, (p) over bar and He) from the usual light elements (Be, B, C, N, and O). We find that the propagation parameters that best-fit p, (p) over bar, and He data are significantly different from those that fit light elements, including the B/C and Be-10/Be-9 secondary-to-primary ratios normally used to calibrate propagation parameters. This suggests that 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.
%K astroparticle physics
%K cosmic rays
%K diffusion
%K Galaxy: general
%K ISM: general
%K methods: statistical
%R 10.3847/0004-637X/824/1/16
%U http://arxiv.org/abs/1602.02243
%U https://doi.org/10.3847/0004-637X/824/1/16
%P 16-19pp