  The discovery of new objects in modern wide-field asteroid and comet surveys
can be enhanced by first identifying observations belonging to known solar
system objects. The assignation of new observations to a known object is an
attribution problem that occurs when a least squares orbit already exists for
the object but a separate fit is not possible to just the set of new
observations. In this work we explore the strongly asymmetric attribution
problem in which the existing least squares orbit is very well constrained and
the new data are sparse. We describe an attribution algorithm that introduces
new quality control metrics in the presence of strong biases in the astrometric
residuals. The main biases arise from the stellar catalogs used in the
reduction of asteroid observations and we show that a simple debiasing with
measured regional catalog biases significantly improves the results. We tested
the attribution algorithm using data from the PS1 survey that used the 2MASS
star catalog for the astrometric reduction. We found small but statistically
significant biases in the data of up to 0.1 arcsec that are relevant only when
the observations reach the level of accuracy made possible by instruments like
PS1. The false attribution rate was measured to be < 1/1,000 with a simple
additional condition that can reduce it to zero while the attribution
efficiency is consistent with 100%.
