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

Identifying False Alarms in the Kepler Planet Candidate Catalog