科研之友
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摘要
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 HVSs 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 catalogue. With no pre-selection of data, we could exclude immediately similar to 99 per cent of the stars in the catalogue and find 80 candidates with more than 90 per cent predicted probability to be HVSs, based only on their position, proper motions and parallax. We have crosschecked our findings with other spectroscopic surveys, determining radial velocities for 30 and spectroscopic distances for five 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 five of these have a probability of > 50 per cent of being unbound from the Milky Way. Tracing back their orbits in different Galactic potential models, we find one possible unbound HVS with v similar to 520 km s(-1), five bound HVSs and, notably, five 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.
- 出版日期2017-9
