FM8p.20 — Inferring Planet Occurrence Rates With a Q1-Q17 Kepler Planet Candidate Catalog Produced by a Machine Learning Classifier

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Aug 4th at 6:00 PM until 7:30 PM

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Author(s): Joseph Catanzarite3, Jon Michael Jenkins2, Sean D. McCauliff4, Christopher Burke3, Steve Bryson2, Natalie Batalha2, Jeffrey Coughlin3, Jason Rowe3, fergal mullally3, susan thompson3, Shawn Seader3, Joseph Twicken3, Jie Li3, robert morris3, jeffrey smith3, michael haas2, jessie christiansen1, Bruce Clarke3

Institution(s): 1. California Institute of Technology, 2. NASA Ames Research Center, 3. SETI Institute, 4. Wyle

NASA’s Kepler Space Telescope monitored the photometric variations of over 170,000 stars, at half-hour cadence, over its four-year prime mission. The Kepler pipeline calibrates the pixels of the target apertures for each star, produces light curves with simple aperture photometry, corrects for systematic error, and detects threshold-crossing events (TCEs) that may be due to transiting planets. The pipeline estimates planet parameters for all TCEs and computes diagnostics used by the Threshold Crossing Event Review Team (TCERT) to produce a catalog of objects that are deemed either likely transiting planet candidates or false positives.
We created a training set from the Q1-Q12 and Q1-Q16 TCERT catalogs and an ensemble of synthetic transiting planets that were injected at the pixel level into all 17 quarters of data, and used it to train a random forest classifier. The classifier uniformly and consistently applies diagnostics developed by the Transiting Planet Search and Data Validation pipeline components and by TCERT to produce a robust catalog of planet candidates.
The characteristics of the planet candidates detected by Kepler (planet radius and period) do not reflect the intrinsic planet population. Detection efficiency is a function of SNR, so the set of detected planet candidates is incomplete. Transit detection preferentially finds close-in planets with nearly edge-on orbits and misses planets whose orbital geometry precludes transits. Reliability of the planet candidates must also be considered, as they may be false positives. Errors in detected planet radius and in assumed star properties can also bias inference of intrinsic planet population characteristics.
In this work we infer the intrinsic planet population, starting with the catalog of detected planet candidates produced by our random forest classifier, and accounting for detection biases and reliabilities as well as for radius errors in the detected population.
Kepler was selected as the 10th mission of the Discovery Program. Funding for this mission is provided by NASA, Science Mission Directorate.