We search for the fastest stars in the subset of stars with radial velocity measurements of the second data release (DR2) of the European Space Agency mission Gaia . Starting from the observed positions, parallaxes, proper motions, and radial velocities, we construct the distance and total velocity distribution of more than 7 million stars in our Milky Way, deriving the full 6D phase space information in Galactocentric coordinates. These information are shared in a catalogue, publicly available at http://home.strw.leidenuniv. nl/~marchetti/research.html. To search for unbound stars, we then focus on stars with a probability greater than 50% of being unbound from the Milky Way. This cut results in a clean sample of 125 sources with reliable astrometric parameters and radial velocities. Of these, 20 stars have probabilities greater than 80 % of being unbound from the Galaxy. On this latter sub-sample, we perform orbit integration to characterize the stars' orbital parameter distributions. As expected given the relatively small sample size of bright stars, we find no hypervelocity star candidates, stars that are moving on orbits consistent with coming from the Galactic Centre. Instead, we find 7 hyper-runaway star candidates, coming from the Galactic disk. Surprisingly, the remaining 13 unbound stars cannot be traced back to the Galaxy, including two of the fastest stars (around 700 km s −1 ). If conformed, these may constitute the tip of the iceberg of a large extragalactic population or the extreme velocity tail of stellar streams.

The mass assembly history of the Milky Way can inform both theory of galaxy formation and the underlying cosmological model. Thus, observational constraints on the properties of both its baryonic and dark matter contents are sought. Here we show that hypervelocity stars (HVSs) can in principle provide such constraints. We model the observed velocity distribution of HVSs, produced by tidal break-up of stellar binaries caused by Sgr A*. Considering a Galactic Centre (GC) binary population consistent with that inferred in more observationally accessible regions, a fit to current HVS data with significance level > 5% can only be obtained if the escape velocity from the GC to 50 kpc is V G < ∼ 850 km s −1 , regardless of the enclosed mass distribution. When a NFW matter density profile for the dark matter halo is assumed, haloes with V G < ∼ 850 km s −1 are in agreement with predictions in the ΛCDM model and that a subset of models around M 200 ∼ 0.5−1.5×10 12 M ⊙ and r s < ∼ 35 kpc can also reproduce Galactic circular velocity data. HVS data alone cannot currently exclude potentials with V G > 850 km s −1 . Finally, specific constraints on the halo mass from HVS data are highly dependent on the assumed baryonic mass potentials. This first attempt to simultaneously constrain GC and dark halo properties is primarily hampered by the paucity and quality of data. It nevertheless demonstrates the potential of our method, that may be fully realised with the ESA Gaia mission.

Hypervelocity stars (HVSs) are amongst the fastest objects in our Milky Way. These stars are predicted to come from the Galactic center (GC) and travel along unbound orbits across the Galaxy. In the coming years, the ESA satellite Gaia will provide the most complete and accurate catalogue of the Milky Way, with full astrometric parameters for more than 1 billion stars. In this paper, we present the expected sample size and properties (mass, magnitude, spatial, velocity distributions) of HVSs in the Gaia stellar catalogue. We build three Gaia mock catalogues of HVSs anchored to current observations, exploring different ejection mechanisms and GC stellar population properties. In all cases, we predict hundreds to thousands of HVSs with precise proper motion measurements within a few tens of kpc from us. For stars with a relative error in total proper motion below 10%, the mass range extends to ∼ 10M but peaks at ∼ 1 M . The majority of Gaia HVSs will therefore probe a different mass and distance range compared to the current non-Gaia sample. In addition, a subset of a few hundreds to a few thousands of HVSs with M ∼ 3 M will be bright enough to have a precise measurement of the three-dimensional velocity from Gaia alone. Finally, we show that Gaia will provide more precise proper motion measurements for the current sample of HVS candidates. This will help identifying their birthplace narrowing down their ejection location, and confirming or rejecting their nature as HVSs. Overall, our forecasts are extremely encouraging in terms of quantity and quality of HVS data that can be exploited to constrain both the Milky Way potential and the GC properties.

The early third data release (EDR3) of the European Space Agency satellite Gaia provides coordinates, parallaxes, and proper motions for ∼1.47 billion sources in our Milky Way, based on 34 months of observations. The combination of Gaia DR2 radial velocities with the more precise and accurate astrometry provided by Gaia EDR3 makes the best dataset available to search for the fastest nearby stars in our Galaxy. We compute the velocity distribution of ∼7 million stars with precise parallaxes, to investigate the high-velocity tail of the velocity distribution of stars in the Milky Way. We release a catalogue with distances, total velocities, and corresponding uncertainties for all the stars considered in our analysis†. By applying quality cuts on the Gaia astrometry and radial velocities, we identify a clean subset of 94 stars with a probability $P_\mathrm{ub}&gt; 50 \%$ to be unbound from our Galaxy. 17 of these have $P_\mathrm{ub}&gt; 80\%$ and are our best candidates. We propagate these stars in the Galactic potential to characterize their orbits. We find that 11 stars are consistent with being ejected from the Galactic disk, and are possible hyper-runaway star candidates. The other 6 stars are not consistent with coming from a known star-forming region. We investigate the effect of adopting a parallax zero point correction, which strongly impacts our results: when applying this correction, we identify only 12 stars with $P_\mathrm{ub}&gt; 50\%$, 3 of these having $P_\mathrm{ub}&gt; 80\%$. Spectroscopic follow-ups with ground-based telescopes are needed to confirm the candidates identified in this work.

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 ∼ 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 ∼ 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.