  Asteroids are an indelible part of most astronomical surveys though only a
few surveys are dedicated to their detection. Over the years, high cadence
microlensing surveys have amassed several terabytes of data while scanning
primarily the Galactic Bulge and Magellanic Clouds for microlensing events and
thus provide a treasure trove of opportunities for scientific data mining. In
particular, numerous asteroids have been observed by visual inspection of
selected images. This paper presents novel deep learning-based solutions for
the recovery and discovery of asteroids in the microlensing data gathered by
the MOA project. Asteroid tracklets can be clearly seen by combining all the
observations on a given night and these tracklets inform the structure of the
dataset. Known asteroids were identified within these composite images and used
for creating the labelled datasets required for supervised learning. Several
custom CNN models were developed to identify images with asteroid tracklets.
Model ensembling was then employed to reduce the variance in the predictions as
well as to improve the generalisation error, achieving a recall of 97.67%.
Furthermore, the YOLOv4 object detector was trained to localize asteroid
tracklets, achieving a mean Average Precision (mAP) of 90.97%. These trained
networks will be applied to 16 years of MOA archival data to find both known
and unknown asteroids that have been observed by the survey over the years. The
methodologies developed can be adapted for use by other surveys for asteroid
recovery and discovery.
