多个空间观测值的交叉匹配与缺失数据处理
Cross-Matching Multiple Spatial Observations and Dealing with Missing Data
摘要：
交叉匹配空间集群和组织几个天文点源测量从一个或多个调查。理想情况下，每个对象都将在每次调查中找到。不幸的是，观察条件和对象本身不断变化。在某些观测中，甚至一些静止的物体也不见了;有时物体有可变的光通量，有时视宁度更差。在大多数情况下，我们面临着大量的差异，在目标检测之间的调查和观察，在不同的时间内采取相同的调查或仪器。处理这些缺失的观测结果是一个难题。第一步是将未命中分类为短暂的-当对象移动或简单地消失时，掩蔽-当噪声隐藏或破坏对象观测时，或边缘-当对象靠近观测场的边缘时。这种分类和一个空间库来表示和操纵观测足迹，有助于构建一个记录命中和未命中的匹配表。传递闭包将朋友的朋友聚集到对象束中。捆绑包汇总统计信息记录在捆绑包表中。这种设计是斯隆数字巡天交叉匹配设计的演变，该设计比较了在不同时间进行的重叠观测。交叉匹配多个空间观测和处理缺失数据。
英文摘要：
Cross-match spatially clusters and organizes several astronomical point-source measurements from one or more surveys. Ideally, each object would be found in each survey. Unfortunately, the observation conditions and the objects themselves change continually. Even some stationary objects are missing in some observations; sometimes objects have a variable light flux and sometimes the seeing is worse. In most cases we are faced with a substantial number of differences in object detections between surveys and between observations taken at different times within the same survey or instrument. Dealing with such missing observations is a difficult problem. The first step is to classify misses as ephemeral - when the object moved or simply disappeared, masked - when noise hid or corrupted the object observation, or edge - when the object was near the edge of the observational field. This classification and a spatial library to represent and manipulate observational footprints help construct a Match table recording both hits and misses. Transitive closure clusters friends-of-friends into object bundles. The bundle summary statistics are recorded in a Bundle table. This design is an evolution of the Sloan Digital Sky Survey cross-match design that compared overlapping observations taken at different times. Cross-Matching Multiple Spatial Observations and Dealing with Missing Data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
