Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2

TL;DR

This paper introduces a fast machine learning method using Self Organising Maps to distinguish true planetary transits from false positives in Kepler and K2 data, improving candidate ranking efficiency.

Contribution

The study develops and applies a SOM-based classification method for planetary candidates, achieving high accuracy without prior dispositioning, and provides publicly available Python code.

Findings

87.0% success rate in distinguishing planets from false positives

Method is fast, taking minutes on a laptop

Applicable early in mission lifecycle

Abstract

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.