AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES

ABSTRACT In the first three years of operation, the Kepler mission found 3697 planet candidates (PCs) from a set of 18,406 transit-like features detected on more than 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor int...

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Bibliographic Details
Published in:The Astrophysical journal 2015-06, Vol.806 (1), p.1-13
Main Authors: McCauliff, Sean D., Jenkins, Jon M., Catanzarite, Joseph, Burke, Christopher J., Coughlin, Jeffrey L., Twicken, Joseph D., Tenenbaum, Peter, Seader, Shawn, Li, Jie, Cote, Miles
Format: Article
Language:English
Subjects:
Algorithms
astronomical databases: miscellaneous
Automation
binaries: eclipsing
Classification
Errors
Extrasolar planets
Light curve
methods: statistical
planets and satellites: detection
Searching
techniques: photometric
Torrents
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Summary:ABSTRACT In the first three years of operation, the Kepler mission found 3697 planet candidates (PCs) from a set of 18,406 transit-like features detected on more than 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor intensive effort. Additionally, this classification methodology does not yield any information about the quality of PCs; all candidates are as credible as any other. The torrent of exoplanet discoveries will continue after Kepler, because a number of exoplanet surveys will have an even broader search area. This paper presents the application of machine-learning techniques to the classification of the exoplanet transit-like signals present in the Kepler light curve data. Transit-like detections are transformed into a uniform set of real-numbered attributes, the most important of which are described in this paper. Each of the known transit-like detections is assigned a class of PC; astrophysical false positive; or systematic, instrumental noise. We use a random forest algorithm to learn the mapping from attributes to classes on this training set. The random forest algorithm has been used previously to classify variable stars; this is the first time it has been used for exoplanet classification. We are able to achieve an overall error rate of 5.85% and an error rate for classifying exoplanets candidates of 2.81%.
ISSN:0004-637X
1538-4357
1538-4357
DOI:10.1088/0004-637X/806/1/6