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.
@article{arxiv.2104.03411,
title = {A New Blind Asteroid Detection Scheme},
author = {Nathan Golovich and Noah Lifset and Robert Armstrong and Eric Green and Michael D. Schneider and Roger Pearce},
journal= {arXiv preprint arXiv:2104.03411},
year = {2021}
}