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A Machine Learning Technique to Identify Transit Shaped Signals

Authors
Type
Published Article
Journal
The Astrophysical Journal
Publisher
American Astronomical Society
Publication Date
Oct 01, 2015
Volume
812
Issue
1
Pages
46–46
Identifiers
DOI: 10.1088/0004-637X/812/1/46
Source
SETI Institute
License
Green

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 ...

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