The Asteroid Identification Problem IV: Attributions

Abstract

Existing archives of asteroid observations contain many objects with very short observed arcs. In this paper we present a method that we have used with considerable success to attribute these short arc “discoveries” to other objects with better defined orbits. The method consists of a three-stage filtering process whereby several billion possible attribution/orbit pairs are systematically analyzed with more and more exact algorithms, at each stage rejecting improbable cases. The first stage compares an attributable, by definition a synthetic observation representative of all the observations over a short arc, with the predicted observation for each available orbit. The second stage compares the proposed attributable observations with predicted positions from the known orbit using conventional linear covariance techniques, considering both the position and motion on the celestial sphere. In the final filter we attempt to compute a best-fitting orbit by differential corrections using the combined dataset. With this algorithm we have found 1675 attributions in approximately one year of operations, in addition to 902 identifications found with another algorithm. We discuss the lessons learned from this one-year experiment and the possibilities of further improvement and automation of the procedure.