银河系Cosmic Ray物理学的马尔可夫链蒙特卡罗：I.漏箱模型的方法和结果
A Markov Chain Monte Carlo for Galactic Cosmic Ray physics: I. Method and results for the Leaky Box Model
摘要：
带电宇宙射线在银河系中的传播取决于传输参数，其数量可能很大，这取决于仔细研究的传播模型。用于确定这些参数的标准方法是手动扫描，导致参数空间的低效和不完整覆盖。我们实现了马尔可夫链蒙特卡罗（MCMC），这是非常适合于多参数的确定。它的特性（老化长度，接受度和相关长度）在现象学上很好理解的漏盒模型中进行了讨论。从技术的角度来看，一个试验功能的基础上二进制空间分区被认为是非常有效的，允许同时确定多达9个参数，包括运输和源参数，如斜率和丰度。我们的最佳拟合模型包括低能量截止和再加速，其值与扩散模型中的值一致。排除扩散斜率（delta=1/3）的Kolmogorov谱。δ和α（源光谱的斜率）的边缘化概率密度函数为δ ~0.55-0.60和α ~2.14-2.17，这取决于所使用的数据集和拟合中的自由参数的数量。所有的源谱参数（斜率和丰度）之间呈正相关，并与再加速强度，但与其他传播参数呈负相关。一个即将到来的研究将我们的分析扩展到更多的物理扩散模型。
英文摘要：
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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