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.
We perform smoothed-particle hydrodynamical simulations of the explosion of a helium star in a close binary system, and study the effects of the explosion on the companion star as well as the effect of the presence of the companion on the supernova remnant. By simulating the mechanism of the supernova from just after core bounce until the remnant shell passes the stellar companion, we are able to separate the various phenomena leading to the final system parameters. In the final system, we measure the mass stripping and ablation from, and the additional velocity imparted to, the companion stars. Our results agree with recent work showing smaller values for these quantities compared to earlier estimates. We do find some differences, however, particularly in the velocity gained by the companion, which can be explained by the different ejecta structure that naturally results from the explosion in our simulations. These results indicate that predictions based on extrapolated Type Ia simulations should be revised. We also examine the structure of the supernova ejecta shell. The presence of the companion star produces a conical cavity in the expanding supernova remnant, and loss of material from the companion causes the supernova remnant to be more metal-rich on one side and more hydrogen-rich (from the companion material) around the cavity. Following the impact of the shell, we examine the state of the companion after being heated by the shock.
There is mounting observational evidence that most galactic nuclei host both supermassive black holes (SMBHs) and young populations of stars. With an abundance of massive stars, core-collapse supernovae are expected in SMBH spheres of influence. We develop a novel numerical method, based on the Kompaneets approximation, to trace supernova remnant (SNR) evolution in these hostile environments, where radial gas gradients and SMBH tides are present. We trace the adiabatic evolution of the SNR shock until 50% of the remnant is either in the radiative phase or is slowed down below the SMBH Keplerian velocity and is sheared apart. In this way, we obtain shapes and lifetimes of SNRs as a function of the explosion distance from the SMBH, the gas density profile and the SMBH mass. As an application, we focus here exclusively on quiescent SMBHs, because their light may not hamper detections of SNRs and because we can take advantage of the unsurpassed detailed observations of our Galactic Centre. Assuming that properties such as gas and stellar content scale appropriately with the SMBH mass, we study SNR evolution around other quiescent SMBHs. We find that, for SMBH masses over ∼ 10 7 M ⊙ , tidal disruption of SNRs can occur at less than 10 4 yr, leading to a shortened X-ray emitting adiabatic phase, and to no radiative phase. On the other hand, only modest disruption is expected in our Galactic Centre for SNRs in their X-ray stage. This is in accordance with estimates of the lifetime of the Sgr A East SNR, which leads us to expect one supernova per 10 4 yr in the sphere of influence of Sgr A*.
Appreciable star formation, and, therefore, numerous massive stars, are frequently found near supermassive black holes (SMBHs). As a result, core-collapse supernovae in these regions should also be expected. In this paper, we consider the observational consequences of predicting the fate of supernova remnants (SNRs) in the sphere of influence of quiescent SMBHs. We present these results in the context of 'autarkic' nuclei, a model that describes quiescent nuclei as steady-state and self-sufficient environments where the SMBH accretes stellar winds with no appreciable inflow of material from beyond the sphere of influence. These regions have properties such as gas density that scale with the mass of the SMBH. Using predictions of the X-ray lifetimes of SNRs originating in the sphere of influence, we make estimates of the number of core collapse SNRs present at a given time. With the knowledge of lifetimes of SNRs and their association with young stars, we predict a number of core-collapse SNRs that grows from ∼ 1 around Milky Way-like (4.3 × 10 6 M ⊙ ) SMBHs to ∼ 100 around the highest mass (10 10 M ⊙ ) SMBHs. The presence of young SNRs will amplify the X-ray emission near quiescent SMBHs, and we show that the total core-collapse SNR emission has the potential to influence soft X-ray searches for very low-luminosity SMBHs. Our SNR lifetime estimates also allow us to predict star formation rates in these regions. Assuming a steady-state replenishment of massive stars, we estimate a star formation rate density of 2×10 −4 M ⊙ yr −1 pc −2 around the Milky Way SMBH, and a similar value around other SMBHs due to a weak dependence on SMBH mass. This value is consistent with currently available observations.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.