Institution(s): 1. Princeton University
Beginning with Kepler, but continuing with K2 and TESS, transiting planet candidates are now found at a much faster rate than follow-up observations can be obtained. Thus, distinguishing true planet candidates from astrophysical false positives has become primarily a statistical exercise. I will describe a new publicly available open-source Python package for analyzing the astrophysical false positive probabilities of transiting exoplanet signals. In addition, I will present results of applying this analysis to both Kepler and K2 planet candidates, resulting in the probabilistic validation of thousands of exoplanets, as well as identifying many likely false positives.