A Markov Chain Monte Carlo for Galactic Cosmic Ray physics: I. Method and results for the Leaky Box Model

Details A Markov Chain Monte Carlo for Galactic Cosmic Ray physics: I. Method and results for the Leaky Box Model A Markov Chain Monte Carlo for Galactic Cosmic Ray physics: I. Method and results for the Leaky Box Model

2008-08-18

arxiv.org/abs/0808.2437v2

A&A 497, 991 (2009)

Astronomy and Astrophysics

Astrophysics

18 pages, 15 figures, one brief section 2 added on the method (matches the A&A accepted version)

Scientific paper

10.1051/0004-6361/200810824

Propagation of charged cosmic-rays in the Galaxy depends on the transport parameters, whose number can be large depending on the propagation model under scrutiny. A standard approach for determining these parameters is a manual scan, leading to an inefficient and incomplete coverage of the parameter space. We implement a Markov Chain Monte Carlo (MCMC), which is well suited to multi-parameter determination. Its specificities (burn-in length, acceptance, and correlation length) are discussed in the phenomenologically well-understood Leaky-Box Model. From a technical point of view, a trial function based on binary-space partitioning is found to be extremely efficient, allowing a simultaneous determination of up to nine parameters, including transport and source parameters, such as slope and abundances. Our best-fit model includes both a low energy cut-off and reacceleration, whose values are consistent with those found in diffusion models. A Kolmogorov spectrum for the diffusion slope (delta=1/3) is excluded. The marginalised probability-density function for delta and alpha (the slope of the source spectra) are delta~0.55-0.60 and alpha~2.14-2.17, depending on the dataset used and the number of free parameters in the fit. All source-spectrum parameters (slope and abundances) are positively correlated among themselves and with the reacceleration strength, but are negatively correlated with the other propagation parameters. A forthcoming study will extend our analysis to more physical diffusion models.

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