Identifying False Alarms in the Kepler Planet Candidate Catalog

TL;DR

This paper introduces an automated Bayesian method to distinguish true planetary transits from instrumental false alarms in the Kepler catalog, improving the accuracy of planet detection and occurrence rate estimates.

Contribution

The paper presents a novel Bayesian approach for identifying false alarms in Kepler data, enhancing the reliability of planet candidate classification.

Findings

Effective in reducing false positives from instrumental noise.

Improves accuracy of planet occurrence rate calculations.

Validated on simulated and real Kepler data.

Abstract

We present a new automated method to identify instrumental features masquerading as small, long period planets in the \kepler\ planet candidate catalog. These systematics, mistakenly identified as planet transits, can have a strong impact on occurrence rate calculations because they cluster in a region of parameter space where Kepler's sensitivity to planets is poor. We compare individual transit-like events to a variety of models of real transits and systematic events, and use a Bayesian Information Criterion to evaluate the likelihood that each event is real. We describe our technique and test its performance on simulated data. Results from this technique are incorporated in the \kepler\ Q1-17 DR24 planet candidate catalog of \citet{Coughlin15}.