Automatic Classification of Kepler Planetary Transit Candidates

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%.

14 pages, 10 figures

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Cited by in corpus (15)

• The Kepler Follow-Up Observation Program. I. A Catalog of Companions to Kepler Stars from High-Resolution Imaging
• Validation of Twelve Small Kepler Transiting Planets in the Habitable Zone
• Searching for Exoplanets Using Artificial Intelligence
• Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates
• Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
• Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
• The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility
• AutoRegressive Planet Search: Application to the Kepler Mission
• AutoRegressive Planet Search: Methodology
• Detecting Exoplanet Transits through Machine Learning Techniques with Convolutional Neural Networks
• Re-Evaluating Small Long-Period Confirmed Planets From Kepler
• A systematic search for transiting planets in the K2 data
• Potential Vorticity of Saturn's Polar Regions: Seasonality and Instabilities
• ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy
• Advanced Astroinformatics for Variable Star Classification