焦散线交叉双透镜微透镜事件模型选择的贝叶斯算法
A Bayesian algorithm for model selection applied to caustic-crossing binary-lens microlensing events
摘要：
我们提出了一个完整的贝叶斯算法，旨在执行自动搜索的参数空间的焦散交叉二元透镜微透镜事件。这是建立在以前的工作，实现来自银河系模型和几何考虑的先验。先验的几何结构将参数空间划分为定义明确的盒子，我们使用多个蒙特卡洛马尔可夫链进行探索。我们概述了我们的贝叶斯框架和测试我们的自动搜索方案使用两个数据集：一个合成的光变曲线，和OGLE-2007-BLG-472，我们在以前的工作中分析的意见。对于合成数据，我们恢复输入参数。对于OGLE-2007-BLG-472，我们发现，虽然对于具有极长时间尺度的行星质量比模型，χ^2最小，但引入先验和BIC最小化，而不是χ^2，有利于更合理的透镜模型，即距离6.3 kpc，具有0.78和0.11 M_Sun分量的双星，相比之下，我们以前的结果为1.50和0.12 M_Sun在1 kpc的距离。
英文摘要：
We present a full Bayesian algorithm designed to perform automated searches of the parameter space of caustic-crossing binary-lens microlensing events. This builds on previous work implementing priors derived from Galactic models and geometrical considerations. The geometrical structure of the priors divides the parameter space into well-defined boxes that we explore with multiple Monte Carlo Markov Chains. We outline our Bayesian framework and test our automated search scheme using two data sets: a synthetic lightcurve, and the observations of OGLE-2007-BLG-472 that we analysed in previous work. For the synthetic data, we recover the input parameters. For OGLE-2007-BLG-472 we find that while χ^2 is minimised for a planetary mass-ratio model with extremely long timescale, the introduction of priors and minimisation of BIC, rather than χ^2, favours a more plausible lens model, a binary star with components of 0.78 and 0.11 M_Sun at a distance of 6.3 kpc, compared to our previous result of 1.50 and 0.12 M_Sun at a distance of 1 kpc.
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