Hypervelocity stars (HVSs) are characterized by a total velocity in excess of the Galactic escape speed, and with trajectories consistent with coming from the Galactic Centre. We apply a novel data mining routine, an artificial neural network, to discover HVSs in the TGAS subset of the first data release of the Gaia satellite, using only the astrometry of the stars. We find 80 stars with a predicted probability >90% of being HVSs, and we retrieved radial velocities for 47 of those. We discover 14 objects with a total velocity in the Galactic rest frame >400 km s−1, and 5 of these have a probability >50% of being unbound from the Milky Way. Tracing back orbits in different Galactic potentials, we discover 1 HVS candidate, 5 bound HVS candidates, and 5 runaway star candidates with remarkably high velocities, between 400 and 780 km s−1. We wait for future Gaia releases to confirm the goodness of our sample and to increase the number of HVS candidates.
