  In this paper we investigate how implementing machine learning could improve
the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark
Energy Survey (DES) data when used alongside orbit fitting. The discovery of
multiple TNOs that appear to show a similarity in their orbital parameters has
led to the suggestion that one or more undetected planets, an as yet
undiscovered "Planet 9", may be present in the outer Solar System. DES is well
placed to detect such a planet and has already been used to discover many other
TNOs. Here, we perform tests on eight different supervised machine learning
algorithms, using a dataset consisting of simulated TNOs buried within real DES
noise data. We found that the best performing classifier was the Random Forest
which, when optimised, performed well at detecting the rare objects. We achieve
an area under the receiver operating characteristic (ROC) curve, (AUC) $= 0.996
\pm 0.001$. After optimizing the decision threshold of the Random Forest, we
achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by
using the optimized classifier to pre-select objects, we are able to run the
orbit-fitting stage of our detection pipeline five times faster.
