Cross-Matching Multiple Spatial Observations and Dealing with Missing Data

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, 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 a survey. 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.

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TypeTechReport
NumberMSR-TR-2006-175
Pages7
InstitutionMicrosoft Research
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