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Machine learning for searching the dark energy survey for trans-Neptunian objects

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journal contribution
posted on 2023-06-09, 22:48 authored by B Henghes, O Lahav, D W Gerdes, H W Lin, R Morgan, T M C Abbott, M Aguena, S Allam, J Annis, S Avila, E Bertin, D Brooks, D L Burke, Kathy RomerKathy Romer, Reese WilkinsonReese Wilkinson, DES Collaboration, others
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.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Publications of the Astronomical Society of the Pacific

ISSN

0004-6280

Publisher

IOP Publishing

Issue

1019

Volume

133

Page range

1-14

Article number

a014501

Pages

16.0

Department affiliated with

  • Physics and Astronomy Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2021-01-19

First Open Access (FOA) Date

2021-12-11

First Compliant Deposit (FCD) Date

2021-01-18