Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
This work addresses the challenge of finding rare TNOs to aid in the search for potential undiscovered planets like Planet 9, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of efficiently detecting Trans-Neptunian Objects (TNOs) in Dark Energy Survey data by testing machine learning algorithms alongside orbit fitting, achieving an AUC of 0.996, recall of 0.96, precision of 0.80, and speeding up the detection pipeline by five times.
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.