Automatic Classification of Kepler Planetary Transit Candidates

TL;DR

This paper introduces a machine learning approach using random forests to classify Kepler transit signals, significantly reducing manual effort and providing candidate quality assessment.

Contribution

It applies random forest algorithms to classify exoplanet candidates, false positives, and noise, achieving high accuracy and marking the first use of this method in exoplanet classification.

Findings

Overall error rate of 5.85%

Exoplanet candidate classification error of 2.81%

Effective automated classification of Kepler signals

Abstract

In the first three years of operation the Kepler mission found 3,697 planet candidates from a set of 18,406 transit-like features detected on over 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 planet candidates; all candidates are as credible as any other candidate. The torrent of exoplanet discoveries will continue after Kepler as there will be a number of exoplanet surveys that have an even broader search area. This paper presents the application of machine-learning techniques to the classification of 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 planet candidate; 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%.