论文标题

微透镜预测:银河盘动力学模型的影响

Microlensing Predictions: Impact of Galactic Disc Dynamical Models

论文作者

Yang, Hongjing, Mao, Shude, Zang, Weicheng, Zhang, Xiangyu

论文摘要

银河系模型在微透镜领域中起着重要作用,不仅用于分析单个事件,而且对于事件整体的统计数据。但是,该场中使用的银河模型各不相同,有些是不切实际的简化。在这里,我们测试了三个银河盘动力学模型,第一个是一个简单的标准模型,在该领域广泛使用,而其他两个则考虑速度分散体的径向依赖性,而在最后一个模型中,不对称的漂移。我们发现,对于典型的镜头质量$ m _ {\ rm l} = 0.5m _ {\ odot} $,这两个新的动态模型预测$ \ sim16 \%$或$ \ sim5 \%$ sim $ siss long-timesscale timesercale event $ \ sim 3.5 \%$ $短时间尺度事件($ t _ {\ rm e} <3 $ days)比标准型号。此外,微透明事件速率是爱因斯坦半径$θ_ {\ rm e} $或微透明差异等值$π_ {\ rm e} $的函数的函数。这两个新型号还会影响总微透镜事件速率。该结果在一定程度上也会影响对个别事件的贝叶斯分析,但总体而言,影响很小。但是,我们仍然建议建模者在选择银河模型时应该更加谨慎,尤其是在涉及大量事件的贝叶斯分析的统计工作中。此外,我们在$θ_ {\ rm e} $和$π_ {\ rm e} $分布中发现了渐近幂律行为,我们提供了一个简单的模型来理解它们。

Galactic model plays an important role in the microlensing field, not only for analyses of individual events but also for statistics of the ensemble of events. However, the Galactic models used in the field varies, and some are unrealistically simplified. Here we tested three Galactic disc dynamic models, the first is a simple standard model that was widely used in this field, whereas the other two consider the radial dependence of the velocity dispersion, and in the last model, the asymmetric drift. We found that for a typical lens mass $M_{\rm L}=0.5M_{\odot}$, the two new dynamical models predict $\sim16\%$ or $\sim5\%$ less long-timescale events (e.g., microlensing timescale $t_{\rm E}>300$ days) and $\sim 5\%$ and $\sim 3.5\%$ more short-timescale events ($t_{\rm E}<3$ days) than the standard model. Moreover, the microlensing event rate as a function of Einstein radius $θ_{\rm E}$ or microlensing parallax $π_{\rm E}$ also shows some model dependence (a few percent). The two new models also have an impact on the total microlensing event rate. This result will also to some degree affect the Bayesian analysis of individual events, but overall, the impact is small. However, we still recommend that modelers should be more careful when choosing the Galactic model, especially in statistical works involving Bayesian analyses of a large number of events. Additionally, we find the asymptotic power-law behaviors in both $θ_{\rm E}$ and $π_{\rm E}$ distributions, and we provide a simple model to understand them.

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