Bayesian analysis of caustic-crossing microlensing events
Bayesian analysis of caustic-crossing microlensing events
复制标题
焦散穿过微透镜事件的贝叶斯分析
DOI：
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发表时间：
2009
期刊：
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通讯作者：
P. Browne
中科院分区：
文献类型：
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作者：
A. Cassan;K. Horne;N. Kains;Y. Tsapras;P. Browne
目标。焦散线交叉双星-透镜微透镜事件是重要的异常事件，因为它们能够探测到围绕透镜星星运行的太阳系外行星伴星。因此，快速和强大的建模方法在帮助确定一个行星是否被一个事件探测到方面具有首要意义。Cassan引入了一组新的参数来模拟二元透镜事件，这些参数与光变曲线的性质密切相关。在这项工作中，我们解释了如何贝叶斯先验可以添加到这个框架，并调查有趣的选项。方法.我们开发了一个数学公式，使我们能够计算分析的先验的新参数，给定一些以前的知识，其他物理量。我们明确地计算先验的一些有趣的情况下，并显示如何可以实现在一个完全贝叶斯，马尔可夫链蒙特卡罗算法。结果使用贝叶斯先验可以通过减少考虑物理上不可信的模型所花费的时间来加速微透镜拟合代码，并帮助我们根据其参数的物理可验证性来区分替代模型。
Aims. Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping to decide whether a planet is detected by an event. Cassan introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods. We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results. Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.