Estimating Distances from Parallaxes
Estimating Distances from Parallaxes
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DOI：
10.1086/683116
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发表时间：
2015-07
影响因子：
3.5
通讯作者：
C. Bailer-Jones
中科院分区：
文献类型：
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作者：
C. Bailer-Jones
像盖亚和LSST这样的天体测量将测量数亿颗恒星的视差。但他们不会测量一个单一的距离。相反，必须从视差估计距离。在这篇教学文章中，我展示了一旦分数视差误差大于20%，这样做就不是小事了，这将是盖亚目录中大约80%的恒星的情况。估计距离是一个推理问题，其中使用先验假设是不可避免的。我调查的属性和性能的各种先验，并研究其影响。一个假定的无信息的均匀先验距离示出给非常差的距离估计（大的偏差和方差）。任何在一定距离处具有尖锐截止的先验都有类似的问题。先验知识的选择取决于一个人所拥有的信息，并且愿意使用，例如，调查和银河。我证明了一个简单的先验渐近减少到零，在无限远的距离具有良好的性能，容纳非正松弛，并不需要一个偏差校正。
Astrometric surveys such as Gaia and LSST will measure parallaxes for hundreds of millions of stars. Yet they will not measure a single distance. Rather, a distance must be estimated from a parallax. In this didactic article, I show that doing this is not trivial once the fractional parallax error is larger than about 20%, which will be the case for about 80% of stars in the Gaia catalog. Estimating distances is an inference problem in which the use of prior assumptions is unavoidable. I investigate the properties and performance of various priors and examine their implications. A supposed uninformative uniform prior in distance is shown to give very poor distance estimates (large bias and variance). Any prior with a sharp cut-off at some distance has similar problems. The choice of prior depends on the information one has available—and is willing to use—concerning, e.g., the survey and the Galaxy. I demonstrate that a simple prior which decreases asymptotically to zero at infinite distance has good performance, accommodates nonpositive parallaxes, and does not require a bias correction.