A New Blind Asteroid Detection Scheme

TL;DR

This paper introduces an optimal blind asteroid detection method that efficiently identifies faint asteroids in large astronomical datasets by sampling motion parameters and applying signal-matched filtering, outperforming traditional methods.

Contribution

The paper presents a novel detection scheme that samples motion space priors, maps trajectories to data, and uses signal-matched filtering, enabling more efficient asteroid detection in large surveys.

Findings

Successfully recovered most expected asteroids in test data

Discovered hundreds of new asteroids with limited observations

Demonstrated potential for application to larger sky surveys

Abstract

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.