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 ~ { } { AU } , \sigma _ { e } \sim 0.07 and \sigma _ { i } \sim 0. ^ { \circ } 5 ~ { } { 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 .