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

Henghes, B. and Lahav,, O and Gerdes, D.W. and Desai, Shantanu et. al. (2021) Machine learning for searching the dark energy survey for trans-neptunian objects. Publications of the Astronomical Society of the Pacific, 133 (014501). pp. 1-14. ISSN 00046280

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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.

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IITH Creators:
IITH CreatorsORCiD
Desai, Shantanu
Item Type: Article
Uncontrolled Keywords: Kuiper Belt, Trans-Neptunian Objects, Oort Cloud
Subjects: Physics
Physics > Astronomy Astrophysics
Divisions: Department of Physics
Depositing User: . LibTrainee 2021
Date Deposited: 31 May 2021 05:32
Last Modified: 31 May 2021 05:32
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