DART-Vetter distinguishes planetary candidates from false positives detected in any transiting survey, and is tailored for photometric data collected from space-based missions. The Convolutional Neural Network is trained on Kepler and TESS Threshold Crossing Events (TCEs), and processes only light curves folded on the period of the relative signal. DART-Vetter has a simple and compact architecture; it is lightweight enough to be executed on personal laptops.

DAVE implements a pipeline to find and vet planets planets using data from NASA's K2 mission. The pipeline contains several modules tailored to particular aspects of the vetting procedures, using photocenter analysis to rule out background eclipsing binaries and flux time-series analysis to rule out odd–even differences, secondary eclipses, low-S/N events, variability other than a transit, and size of the transiting object.