Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

TL;DR

This paper demonstrates that machine learning, particularly Random Forest classifiers, can significantly improve the efficiency and accuracy of detecting Trans-Neptunian Objects in Dark Energy Survey data, potentially aiding in the search for undiscovered planets.

Contribution

The study evaluates multiple machine learning algorithms and identifies the Random Forest as the most effective for TNO detection, achieving high ROC AUC and speeding up orbit fitting.

Findings

Random Forest achieved ROC AUC of 0.996.

Optimized classifier reached 96% recall and 80% precision.

Pre-selection with ML sped up orbit fitting fivefold.

Abstract

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.