False positive probabilties for all Kepler Objects of Interest: 1284 newly validated planets and 428 likely false positives

Abstract: We present astrophysical false positive probability calculations for every Kepler Object of Interest (KOI)---the first large-scale demonstration of a fully automated transiting planet validation procedure. Out of 7056 KOIs, we determine that 1935 have probabilities <1% to be astrophysical false positives, and thus may be considered validated planets. 1284 of these have not yet been validated or confirmed by other methods. In addition, we identify 428 KOIs likely to be false positives that have not yet been identified as such, though some of these may be a result of unidentified transit timing variations. A side product of these calculations is full stellar property posterior samplings for every host star, modeled as single, binary, and triple systems. These calculations use 'vespa', a publicly available Python package able to be easily applied to any transiting exoplanet candidate.

• The paper introduces an automated Bayesian framework using vespa to calculate false positive probabilities for 7,056 Kepler Objects of Interest.
• It validates 1,284 exoplanet candidates by demonstrating FPPs below 1% and reclassifies 428 signals as likely false positives.
• The methodology enhances reproducibility and streamlines follow-up efforts, paving the way for efficient exoplanet validation in current and future missions.

False Positive Probabilities for All Kepler Objects of Interest

The paper "False Positive Probabilities for all Kepler Objects of Interest" by Morton et al. presents a comprehensive analysis of the false-positive probabilities (FPP) of planetary signals detected by the Kepler mission, a critical step in validating exoplanetary candidates. The research introduces and applies a fully automated method for evaluating the FPPs, using a large dataset comprising 7,056 Kepler Objects of Interest (KOIs). This work significantly enhances the capacity to differentiate between genuine exoplanet detections and false positives, such as eclipsing binaries and other astrophysical misinterpretations.

Methodology

The methodology revolves around a Bayesian framework embedded within the vespa Python package, designed for batch processing of a large number of candidates. The authors utilize a model selection approach, assigning probabilities to multiple hypotheses explaining the transit-like signals observed by Kepler. The principal models used include unblended eclipsing binaries, hierarchical triples, and background eclipsing binaries. Additionally, the study introduces "double-period" versions of these scenarios to account for misinterpretations arising from similar eclipse depths between primary and secondary eclipses. The probabilistic calculations are underpinned by Monte Carlo simulations and Markov Chain Monte Carlo (MCMC) techniques to sample posterior distributions of transit signal parameters. Moreover, vespa provides posterior samplings of host stellar properties, using the isochrones Python module to fit single, binary, and triple star models.

Results

The study identifies 1,935 KOIs with a less than 1% probability of being false positives, thereby classifying them as validated planets. Of these, 1,284 are new validations. Meanwhile, 428 KOIs originally considered candidates are reclassified as likely false positives owing to high FPPs—though it is noted that some may involve transit timing variations (TTVs) yet to be corrected. The research also reveals that planet candidates with significant radii often possess higher FPPs, and candidates in multi-planet systems exhibit lower false positive rates, consistent with previous studies.

Implications

This paper represents a substantial advance in the robustness of exoplanet validation by automating what was historically a labor-intensive follow-up process. The vespa procedure offers reproducibility and a standardized approach applicable across future transiting exoplanet missions such as TESS and PLATO. Practically, this work empowers researchers to discern more accurately between true planetary systems and false positives amidst an ever-growing pool of planet candidates. The public availability of the vespa code further provides a vital tool for the broader exoplanet research community.

Future Directions

The authors acknowledge that while their approach is highly comprehensive, improvements are possible. Future work may involve refining the treatment of blended transiting planets and enhancing vespa's capability to incorporate high-resolution imaging data when assessing FPPs. Additionally, considering planets that may transit unknown or unmonitored stellar companions could also be an area for further exploration.

In conclusion, Morton et al.'s work on quantifying FPPs has doubled the number of confirmed Kepler exoplanets and exemplifies the synergy of data-driven models with observational astrophysics, laying the groundwork for efficient exoplanet validation in current and future observational campaigns.