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Title:
The PS1 Software Systems
Authors:
Heasley, James N.; Jedicke, R.; Magnier, E.
Affiliation:
AA(Univ. of Hawaii), AB(Univ. of Hawaii), AC(Univ. of Hawaii)
Publication:
American Astronomical Society, AAS Meeting #211, #47.17; Bulletin of the American Astronomical Society, Vol. 39, p.806
Publication Date:
12/2007
Origin:
AAS
Bibliographic Code:
2007AAS...211.4717H

Abstract

The Pan-STARRS PS1 observatory will generate on average 1.4 TBytes of image data during a typical night of observing. To support the reduction and analysis of these data, the Pan-STARRS construction project has developed three software systems: the Image Processing Pipeline (IPP) for the reduction and calibration of the images and the generation of source catalogs, the Moving Object Processing System (MOPS), a science client designed to develop orbital information for the moving transient sources found by the IPP, and the Published Science Products Subsystem (PSPS) which will serve as the scientific access point to the catalog data derived by the IPP and as the overall archive for the science products generated by PS1. The IPP has largely been developed internally at the Institute for Astronomy. The software has been extensively tested on CCD mosaic data from the CFH12K, MegaPrime, and Suprime cameras. Since late August 2007 we have been using IPP to process the first images from the PS1 gigapixel camera. MOPS incorporates both legacy code and new software developed for linking observations of objects on different nights into tracklets for orbit determination. The MOPS has been tested with simulations based on our model of the solar system as well as on data from the SpaceWatch observatory. The primary component of the PSPS is the Object Data Manager (ODM) which will serve as the science database for the stationary objects found in the PS1 observations. We anticipate tracking over 5.5 billion objects and 140 billion detections over the 3.5 year mission of PS1. The ODM is leveraging the design work done during the Sloan Digital Sky Survey to scale out a database design to accommodate this volume of data.
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