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A Machine Learning Technique to Identify Transit Shaped Signals

Thompson, Susan E. and Mullally, Fergal and Coughlin, Jeff and Christiansen, Jessie L. and Henze, Christopher E. and Haas, Michael R. and Burke, Christopher J. (2015) A Machine Learning Technique to Identify Transit Shaped Signals. Astrophysical Journal, 812 (1). Art. No. 46. ISSN 0004-637X.

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We describe a new metric that uses machine learning to determine if a periodic signal found in a photometric time series appears to be shaped like the signature of a transiting exoplanet. This metric uses dimensionality reduction and k-nearest neighbors to determine whether a given signal is sufficiently similar to known transits in the same data set. This metric is being used by the Kepler Robovetter to determine which signals should be part of the Q1–Q17 DR24 catalog of planetary candidates. The Kepler Mission reports roughly 20,000 potential transiting signals with each run of its pipeline, yet only a few thousand appear to be sufficiently transit shaped to be part of the catalog. The other signals tend to be variable stars and instrumental noise. With this metric, we are able to remove more than 90% of the non-transiting signals while retaining more than 99% of the known planet candidates. When tested with injected transits, less than 1% are lost. This metric will enable the Kepler mission and future missions looking for transiting planets to rapidly and consistently find the best planetary candidates for follow-up and cataloging.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Thompson, Susan E.0000-0001-7106-4683
Coughlin, Jeff0000-0003-1634-9672
Christiansen, Jessie L.0000-0002-8035-4778
Additional Information:© 2015. The American Astronomical Society. Received 2015 July 1; accepted 2015 August 28; published 2015 October 7. We thank the larger Kepler team for their support and hard work in making this data available and in supporting this paper. Funding for the Kepler mission is provided by the NASA Science Mission Directorate. We also thank the referee for useful and insightful comments that improved the clarity of the manuscript. Some of the data presented in this paper were obtained from the Multi-mission Archive at the Space Telescope Science Institute (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.
Funding AgencyGrant Number
Subject Keywords:binaries: eclipsing; methods: data analysis; planetary systems
Issue or Number:1
Record Number:CaltechAUTHORS:20151208-070323993
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Official Citation:Susan E. Thompson et al 2015 ApJ 812 46
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:62676
Deposited By: Ruth Sustaita
Deposited On:08 Dec 2015 15:58
Last Modified:14 Oct 2019 23:29

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