  The Panoramic Survey Telescope And Rapid Response System (Pan-STARRS) under
development at the University of Hawaii's Institute for Astronomy is creating
the first fully automated end-to-end Moving Object Processing System (MOPS) in
the world. It will be capable of identifying detections of moving objects in
our solar system and linking those detections within and between nights,
attributing those detections to known objects, calculating initial and
differentially-corrected orbits for linked detections, precovering detections
when they exist, and orbit identification. Here we describe new kd-tree and
variable-tree algorithms that allow fast, efficient, scalable linking of intra
and inter-night detections. Using a pseudo-realistic simulation of the
Pan-STARRS survey strategy incorporating weather, astrometric accuracy and
false detections we have achieved nearly 100% efficiency and accuracy for
intra-night linking and nearly 100% efficiency for inter-night linking within a
lunation. At realistic sky-plane densities for both real and false detections
the intra-night linking of detections into `tracks' currently has an accuracy
of 0.3%. Successful tests of the MOPS on real source detections from the
Spacewatch asteroid survey indicate that the MOPS is capable of identifying
asteroids in real data.
