SCIENCE GOALS AND OBJECTIVES THE PAN-STARRS-1 (PS1) AND PAN-STARRS-2 (PS2) TELESCOPES WITH THEIR GIGA-PIXEL CAMERAS ARE CAPABLE OF DETECTING BOTH FAST- AND SLOW-MOVING OBJECTS IN THE SOLAR SYSTEM RANGING FROM NEAR-EARTH OBJECTS (NEOS) TO THE MOST DISTANT TRANS-NEPTUNIAN OBJECTS (TNOS). WE HAVE DEVELOPED A SOFTWARE PIPELINE WHICH UTILIZES A NOVEL HELIOCENTRIC-TRANSFORMATION TECHNIQUE TO SEARCH FOR AND DISCOVER SLOW-MOVING OBJECTS SUCH AS TROJANS CENTAURS AND DISTANT TNOS BRIGHTER THAN MAGNITUDE 23 IN THE PAN-STARRS (PS) DATA. WE PROPOSE TO ADAPT THIS PIPELINE AND ITS HELIOCENTRIC-TRANSFORMATION TECHNIQUE TO FACILITATE THE RECOVERY OF FAST MOVING AND/OR FAINT NEOS FROM PS (AND OTHER) DATA. THE GOAL OF THE FIRST YEAR OF OUR PROPOSAL IS TO DEVELOP AND RELEASE TO THE COMMUNITY A WORKING NEO DETECTION CODE. THIS WILL INCREASE NEO DETECTION EFFICIENCY IN EXTANT SEARCHES (E.G. PS AND CATALINA) AS WELL AS FUTURE DATA-INTENSIVE SURVEYS (E.G. LSST). THE GOAL OF THE SECOND (AND LAST) YEAR OF OUR PROPOSAL IS TO APPLY OUR NIGHT-TO-NIGHT LINKING METHOD AND USE IT TO SEARCH ARCHIVAL DATA (WITH PARTICULAR EMPHASIS ON PS) WITH THE AIM OF SIGNIFICANTLY INCREASING THE COMPLETENESS OF CATALOGS OF (A) MAIN BELT ASTEROIDS AND (B) NEOS: INCREASED COMPLETENESS WILL SIGNIFICANTLY INCREASE THE EFFICIENCY OF ON-GOING AND FUTURE SURVEYS SPEEDING THE ATTAINMENT OF NASA S CONGRESSIONALLY MANDATED TASK OF DETECTING 90% OF NEOS LARGER THAN 140M IN DIAMETER. METHODOLOGY OUR SEARCH ALGORITHM EMPLOYS A NOVEL HELIOCENTRIC-COORDINATE TRANSFORMATION WHICH GREATLY SIMPLIFIES THE CROSS-NIGHT LINKING OF SETS OF NIGHTLY OBSERVATIONS (TRACKLETS). IN PARTICULAR OUR PROPOSED LINKING ALGORITHM CAN OPERATE EFFICIENTLY AND ROBUSTLY IN THE FACE OF THE HIGH FALSE-POSITIVE RATE IN PAN-STARRS DATA. WE PROPOSED TO TEST AND REFINE OUR CURRENT ALGORITHM TO ALLOW IT TO EFFICIENTLY AND ACCURATELY LINK TRACKLETS FROM FAST-MOVING NEOS. THE TEST DATA EMPLOYED WILL BE THE PS1 DATA SET FROM 2010-2014. THE HIGH FALSE-POSITIVE RATE IN THIS DATA PROVIDES A STRENUOUS TEST OF OUR METHOD S ABILITY TO HANDLE LARGE DATA VOLUMES AND TO EFFICIENTLY REMOVE FALSE-POSITIVES. WE EMPHASIZE THAT OUR ALGORITHM'S ITERATION OVER MULTIPLE ASSUMED DISTANCES IS TRIVIALLY PARALLELIZABLE GREATLY AIDING COMPUTATIONAL SPEED AND EFFICIENCY.