ATTRIBUTIONS

**Andrea Milani
e-mail: milani@dm.unipi.it
Maria Eugenia Sansaturio
e-mail: marsan@wmatem.eis.uva.es
Steven R. Chesley
e-mail: steve.chesley@jpl.nasa.gov
Dipartimento di Matematica, Università di Pisa
Via Buonarroti 2
56127 PISA, ITALY
E.T.S. de Ingenieros Industriales, University of Valladolid
Paseo del Cauce s/n
47011 VALLADOLID, SPAIN
Navigation & Mission Design Section, Jet Propulsion Laboratory
Pasadena, California 91109, USA**

**Revised version January 3, 2001
Manuscript pages: 20; Figures: 6; Tables: 2.**

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 and using
the combined dataset. With this algorithm we have found 1,626
attributions in approximately one year of operations, on top of 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.
**Running title:** Asteroid identification: attributions

- 1. Introduction
- 2. The filtering procedure
- 3. Results
- 4. Conclusions and future work
- Bibliography
- About this document ...