Bayesian analysis of cosmic-ray propagation: evidence against homogeneous diffusion

TL;DR

This study uses Bayesian methods and machine learning to analyze cosmic ray propagation, revealing that different nuclei suggest distinct propagation parameters, challenging the standard homogeneous diffusion assumption.

Contribution

First comprehensive Bayesian analysis separating low-mass isotopes from light elements, showing different propagation parameters and questioning the homogeneous diffusion model.

Findings

Propagation parameters differ significantly between isotope groups.

Standard B/C calibration may lead to incorrect propagation estimates.

New propagation parameters will be included in GALPROP updates.

Abstract

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.