  As astronomical photometric surveys continue to tile the sky repeatedly, the
potential to pushdetection thresholds to fainter limits increases; however,
traditional digital-tracking methods cannotachieve this efficiently beyond time
scales where motion is approximately linear. In this paper weprototype an
optimal detection scheme that samples under a user defined prior on a
parameterizationof the motion space, maps these sampled trajectories to the
data space, and computes an optimalsignal-matched filter for computing the
signal to noise ratio of trial trajectories. We demonstrate thecapability of
this method on a small test data set from the Dark Energy Camera. We recover
themajority of asteroids expected to appear and also discover hundreds of new
asteroids with only a fewhours of observations. We conclude by exploring the
potential for extending this scheme to larger datasets that cover larger areas
of the sky over longer time baselines.
