  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.
