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

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.

Accepted for publication in MNRAS Nov 4th 2016. Full tables available in source files. 10 pages

References in corpus (8)

• The K2 Mission: Characterization and Early results
• Modeling Kepler transit light curves as false positives: Rejection of blend scenarios for Kepler-9, and validation of Kepler-9d, a super-Earth-size planet in a multiple system
• EVEREST: Pixel Level Decorrelation of K2 Light curves
• 197 Candidates and 104 Validated Planets in K2's First Five Fields
• PASTIS: Bayesian extrasolar planet validation. I. General framework, models, and performance
• SOPHIE velocimetry of Kepler transit candidates VII. A false-positive rate of 35% for Kepler close-in giant exoplanet candidates
• Automatic Classification of Kepler Planetary Transit Candidates
• K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0

Cited by in corpus (18)

• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Searching for Exoplanets Using Artificial Intelligence
• ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets
• Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates
• Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
• Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
• Disproval of the validated planets K2-78b, K2-82b, and K2-92b
• Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks
• Transit least-squares survey IV. Earth-like transiting planets expected from the PLATO mission
• Re-Evaluating Small Long-Period Confirmed Planets From Kepler
• Detecting Exoplanet Transits through Machine Learning Techniques with Convolutional Neural Networks
• Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner
• Then and now: A new look at the eclipse timing variations of hierarchical triple star candidates in the primordial $Kepler$-field, revisited by TESS
• Nigraha: Machine-learning based pipeline to identify and evaluate planet candidates from TESS
• Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals
• One-dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets
• ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data
• YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) II: Using Self-Organising Maps to explore young star variability in Sectors 1-13 of TESS data