Bayesian analysis of cosmic-ray propagation parameters: secondary antiparticles from spatial-dependent diffusion models

TL;DR

This paper uses Bayesian methods to analyze cosmic-ray antiparticle spectra, accounting for spatial-dependent diffusion, and finds that antiproton data align with secondary production models, while positron excess suggests additional sources.

Contribution

It introduces a Bayesian analysis of cosmic-ray antiparticles with spatial-dependent diffusion models, providing new insights into secondary production and spectral features.

Findings

Antiproton fluxes are consistent with secondary production predictions.

Positron excess indicates the need for additional sources.

Spatial-dependent diffusion models reproduce observed spectral hardening.

Abstract

The antiparticle energy spectra of Galactic cosmic rays (CRs) have several exciting features such as the unexpected positron excess at $E\sim$10-200\,GeV and the remarkably hard antiproton flux at $E\sim$\,60--450\,GeV recently measured by the \AMS{} experiment. In this paper, we report calculations of antiparticle CR spectra arising from secondary production and their corresponding uncertainties. Using the most recent data on CR protons, helium, carbon, and nuclear ratios $^{10}$Be/$^{9}$Be and B/C, we have performed a global Bayesian analysis, based on a Markov Chain Monte Carlo sampling algorithm, under a scenario of spatial-dependent CR diffusion in the Galaxy which reproduces well the observed spectral hardening in the CR hadron fluxes. While the high-energy positron excess requires the contribution of additional unknown sources, we found that the antiproton data are consistent within the estimated uncertainties, with our predictions based on secondary production.