  Exoplanets in protoplanetary disks cause localized deviations from Keplerian
velocity in channel maps of molecular line emission. Current methods of
characterizing these deviations are time consuming, and there is no unified
standard approach. We demonstrate that machine learning can quickly and
accurately detect the presence of planets. We train our model on synthetic
images generated from simulations and apply it to real observations to identify
forming planets in real systems. Machine learning methods, based on computer
vision, are not only capable of correctly identifying the presence of one or
more planets, but they can also correctly constrain the location of those
planets.
