The paucity of hypervelocity stars ( HVSs ) known to date has severely hampered their potential to investigate the stellar population of the Galactic Centre and the Galactic Potential .
The first Gaia data release ( DR1 , 2016 September 14 ) gives an opportunity to increase the current sample .
The challenge is the disparity between the expected number of hypervelocity stars and that of bound background stars .
We have applied a novel data mining algorithm based on machine learning techniques , an artificial neural network , to the Tycho- Gaia astrometric solution ( TGAS ) catalogue .
With no pre-selection of data , we could exclude immediately \sim 99 \% of the stars in the catalogue and find 80 candidates with more than 90 % predicted probability to be HVSs , based only on their position , proper motions , and parallax .
We have cross-checked our findings with other spectroscopic surveys , determining radial velocities for 30 and spectroscopic distances for 5 candidates .
In addition , follow-up observations have been carried out at the Isaac Newton Telescope for 22 stars , for which we obtained radial velocities and distance estimates .
We discover 14 stars 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 their orbits in different Galactic potential models we find one possible unbound HVS with v \sim 520 km s ^ { -1 } , 5 bound HVSs , and , notably , 5 runaway stars with median velocity between 400 and 780 km s ^ { -1 } .
At the moment , uncertainties in the distance estimates and ages are too large to confirm the nature of our candidates by narrowing down their ejection location , and we wait for future Gaia releases to validate the quality of our sample .
This test successfully demonstrates the feasibility of our new data mining routine .