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

Henghes, B; Lahav, O; Gerdes, DW; Lin, HW; Morgan, R; Abbott, TMC; Aguena, M; ... Wilkinson, RD; + view all (2021) Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects. Publications of the Astronomical Society of the Pacific , 133 (1019) , Article 014501. 10.1088/1538-3873/abcaea.

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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 data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 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.

Type: Article
Title: Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
DOI: 10.1088/1538-3873/abcaea
Publisher version: https://doi.org/10.1088/1538-3873/abcaea
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Science & Technology, Physical Sciences, Astronomy & Astrophysics, Trans-Neptunian objects, Minor planets, Random Forests, Computational methods
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10118532
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