http://arxiv.org/abs/1611.01968
A crucial step in planet hunting surveys is to select the best candidates for follow up observations, given limited telescope resources. This is often performed by human `eyeballing’, a time consuming and statistically awkward process. Here we present a new, fast machine learning technique to separate true planet signals from astrophysical false positives. We use Self Organising Maps (SOMs) to study the transit shapes of \emph{Kepler} and \emph{K2} known and candidate planets. We find that SOMs are capable of distinguishing known planets from known false positives with a success rate of 87.0\%, using the transit shape alone. Furthermore, they do not require any candidates to be dispositioned prior to use, meaning that they can be used early in a mission’s lifetime. A method for classifying candidates using a SOM is developed, and applied to previously unclassified members of the \emph{Kepler} KOI list as well as candidates from the \emph{K2} mission. The method is extremely fast, taking minutes to run the entire KOI list on a typical laptop. We make \texttt{Python} code for performing classifications publicly available, using either new SOMs or those created in this work. The SOM technique represents a novel method for ranking planetary candidate lists, and can be used both alone or as part of a larger autovetting code.
D. Armstrong, D. Pollacco and A. Santerne
Tue, 8 Nov 16
61/75
Comments: Accepted for publication in MNRAS Nov 4th 2016. Full tables available in source files. 10 pages
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