  We report on the serendipitous observations of Solar System objects imaged
during the High cadence Transient Survey (HiTS) 2014 observation campaign. Data
from this high cadence, wide field survey was originally analyzed for finding
variable static sources using Machine Learning to select the most-likely
candidates. In this work we search for moving transients consistent with Solar
System objects and derive their orbital parameters.
  We use a simple, custom detection algorithm to link trajectories and assume
Keplerian motion to derive the asteroid's orbital parameters. We use known
asteroids from the Minor Planet Center (MPC) database to assess the detection
efficiency of the survey and our search algorithm. Trajectories have an average
of nine detections spread over 2 days, and our fit yields typical errors of
$\sigma_a\sim 0.07 ~{\rm AU}$, $\sigma_{\rm e} \sim 0.07 $ and $\sigma_i\sim
0.^{\circ}5~ {\rm deg}$ in semi-major axis, eccentricity, and inclination
respectively for known asteroids in our sample. We extract 7,700 orbits from
our trajectories, identifying 19 near Earth objects, 6,687 asteroids, 14
Centaurs, and 15 trans-Neptunian objects. This highlights the complementarity
of supernova wide field surveys for Solar System research and the significance
of machine learning to clean data of false detections. It is a good example of
the data--driven science that LSST will deliver.
