Statistics – Computation
Scientific paper
Sep 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008dps....40.5206m&link_type=abstract
American Astronomical Society, DPS meeting #40, #52.06; Bulletin of the American Astronomical Society, Vol. 40, p.493
Statistics
Computation
Scientific paper
The Moving Object Pipeline System (MOPS) is the solar system object search and discovery system designed for the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Large Synoptic Survey Telescope (LSST). While the observational schedules of the two telescopes are designed with solar system object search in mind, other science objectives result in some schedules which prove challenging for existing MOPS algorithms. In particular, we focus on the "deep stack" schedules in which a single area of the sky is imaged many times in rapid succession. These "deep stack" collections are a poor match for existing algorithms used in MOPS, resulting in long computation times and unacceptably large memory usage. We present and compare two new methods for efficiently extracting useful data from detections in "deep stack" images and integrating it into the existing MOPS system and we report our experiences with each. One is a modified intra-nightly linking algorithm which uses a partial Hough transform method for quickly identifying multiple detections which follow similar linear motion using data local to each detection. The other is a new post-processing step for intra-nightly linking which uses a KD-Tree of physically plausible pairs of detections to identify pairs with similar vectors of linear motion. Both methods have greatly reduced memory requirements compared to the existing methods, and have been successful in simulations of MOPS using both Pan-STARRS and LSST parameters.
Axelrod Tim
Denneau Larry
Jedicke Robert
Kubica Jeremy
Myers Jonathan Ashley
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