Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys

Abstract

Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of the present paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test datasets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine learning methods including Random Forest Classifiers (RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data ~90% of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.

15 pages, 9 figures, accepted for publication in MNRAS

References in corpus (43)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• Gaia Data Release 2. Summary of the contents and survey properties
• The Gaia mission
• The Transiting Exoplanet Survey Satellite
• The PLATO 2.0 Mission
• The WASP Project and the SuperWASP Cameras
• batman: BAsic Transit Model cAlculatioN in Python
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• A box-fitting algorithm in the search for periodic transits
• The CoRoT satellite in flight : description and performance
• Correcting systematic effects in a large set of photometric lightcurves
• The Kilodegree Extremely Little Telescope (KELT): A Small Robotic Telescope for Large-Area Synoptic Surveys
• Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
• Efficient identification of exoplanetary transit candidates from SuperWASP light curves
• A Trend Filtering Algorithm for wide field variability surveys
• A fast hybrid algorithm for exoplanetary transit searches
• HATSouth: a global network of fully automated identical wide-field telescopes
• Expected Detection and False Alarm Rates for Transiting Jovian Planets
• Random forest automated supervised classification of Hipparcos periodic variable stars
• Searching for pulsars using image pattern recognition
• Searching for Exoplanets Using Artificial Intelligence
• A recurrent neural network for classification of unevenly sampled variable stars
• HAT Variability Survey in the High Stellar Density "Kepler Field" with Millimagnitude Image Subtraction Photometry
• Automatic Classification of Kepler Planetary Transit Candidates
• Deep Learning Classification in Asteroseismology
• Hierarchical Bayesian calibration of tidal orbit decay rates among hot Jupiters
• Machine learning search for variable stars
• Machine Learning Classification of SDSS Transient Survey Images
• An analysis of feature relevance in the classification of astronomical transients with machine learning methods
• Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
• Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer
• Automated classification of Hipparcos unsolved variables
• SIDRA: a blind algorithm for signal detection in photometric surveys
• Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS
• Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
• Automatic classification of time-variable X-ray sources
• Exploring the Long-Term Evolution of GRS 1915+105
• Three small planets transiting the bright young field star K2-233
• Photometric Classification of quasars from RCS-2 using Random Forest
• Discovery of WASP-174b: Doppler tomography of a near-grazing transit
• Transit Clairvoyance: Enhancing TESS follow-up using artificial neural networks
• Trawling for transits in a sea of noise: A Search for Exoplanets by Analysis of WASP Optical Lightcurves and Follow-up (SEAWOLF)

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• Two new HATNet hot Jupiters around A stars, and the first glimpse at the occurrence rate of hot Jupiters from TESS
• Identification of Young Stellar Object candidates in the $Gaia$ DR2 x AllWISE catalogue with machine learning methods
• 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
• WASP-186 and WASP-187: two hot Jupiters discovered by SuperWASP and SOPHIE with additional observations by TESS
• Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks
• Characterizing the local relation between star formation rate and gas-phase metallicity in MaNGA spiral galaxies
• On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
• A search for transiting planets around FGKM dwarfs and sub-giants in the TESS Full Frame Images of the Southern ecliptic hemisphere
• Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
• Classifying Exoplanet Candidates with Convolutional Neural Networks: Application to the Next Generation Transit Survey
• AutoRegressive Planet Search: Methodology
• Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations
• Discovery of five new Galactic symbiotic stars in the VPHAS+ survey
• Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
• Star formation and morphological properties of galaxies in the Pan-STARRS $3\pi$ survey- I. A machine learning approach to galaxy and supernova classification
• 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
• Machine learning prediction for mean motion resonance behaviour -- The planar case
• Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks
• Two transiting hot Jupiters from the WASP survey: WASP-150b and WASP-176b
• The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
• Machine Learning the Fates of Dark Matter Subhalos: A Fuzzy Crystal Ball
• Machine-learning the spectral function of a hole in a quantum antiferromagnet
• DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates
• Large-step neural network for learning the symplectic evolution from partitioned data
• Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation