VizieR Online Data Catalog: Transit metric for Q1-Q17 Kepler candidates (Thompson+, 2015)
Abstract
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 20000 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.
(1 data file).- Publication:
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VizieR Online Data Catalog
- Pub Date:
- February 2016
- DOI:
- 10.26093/cds/vizier.18120046
- Bibcode:
- 2016yCat..18120046T
- Keywords:
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- Planets;
- Stars: double and multiple