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Description
We present a framework to conservatively estimate the probability that any particular planet-like transit signal observed by the Kepler mission is in fact a planet, prior to any ground-based follow-up efforts. We use Monte Carlo methods based on stellar population synthesis and Galactic structure models, and report false positive probabilities (FPPs) for every Kepler Object of Interest, assuming a 20% intrinsic occurrence rate of close-in planets in the radius range 0.5R_{earth}_<R_p_<20R_{earth}_. Nearly 90% of the 1235 candidates have FPP<10%, and over half have FPP<5%. This probability varies with the magnitude and Galactic latitude of the target star, and with the depth of the transit signal - deeper signals generally have higher FPPs than shallower signals. We establish that a single deep high-resolution image will be an effective follow-up tool for the shallowest (Earth-sized) transits, providing the quickest route toward probabilistically validating the smallest candidates by potentially decreasing the FPP of an Earth-sized transit around a faint star from >10% to <1%. Since Kepler has detected many more planetary signals than can be positively confirmed with ground-based follow-up efforts in the near term, these calculations will be crucial to using the ensemble of Kepler data to determine population characteristics of planetary systems. We also describe how our analysis complements the Kepler team's more detailed BLENDER false positive analysis for planet validation.
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