Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNew nearby hypervelocity stars and their spatial distribution from Gaia DR2
84
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Base on about 4,500 large tangential velocity ($V_mathrm{tan}>0.75V_mathrm{esc}$) with high-precision proper motions and $5sigma$ parallaxes in Gaia DR2 5D information derived from parallax and proper motion, we identify more than 600 high velocity stars with $50%$ unbound probability. Of these, 28 nearby (less than 6 kpc) late-type Hypervelocity stars (HVSs) with over $99%$ possibility of unbound are discovered. In order to search for the unbound stars from the full Gaia DR2 6D phase space information derived from parallax, proper motion and radial velocity, we also identify 28 stars from the total velocity ($V_mathrm{gc}>0.75V_mathrm{esc}$) that have probabilities greater than $50%$ of being unbound from the Galaxy. Of these, only three have a nearly $99%$ probabilities of being unbound. On the whole HVSs subsample, there is 12 sources reported by other surveys. We study the spatial distribution of angular positions and angular separation of HVSs. We find the unbound HVSs are spatially anisotropic that is most significant in the Galactic longitude at more than $3sigma$ level, and lower unbound probability HVSs are systematically more isotropic. The spatial distribution can reflect the origin of HVSs and we discuss the possible origin link with the anisotropy.
rate research
Read More
Hypervelocity stars are intriguing rare objects traveling at speeds large enough to be unbound from the Milky Way. Several mechanisms have been proposed for producing them, including the interaction of the Galaxys super-massive black hole (SMBH) with a binary; rapid mass-loss from a companion to a star in a short-period binary; the tidal disruption of an infalling galaxy and finally ejection from the Large Magellanic Cloud. While previously discovered high-velocity early-type stars are thought to be the result of an interaction with the SMBH, the origin of high-velocity late type stars is ambiguous. The second data release of Gaia (DR2) enables a unique opportunity to resolve this ambiguity and determine whether any late-type candidates are truly unbound from the Milky Way. In this paper, we utilize the new proper motion and velocity information available from DR2 to re-evaluate a collection of historical data compiled on the newly-created Open Fast Stars Catalog. We find that almost all previously-known high-velocity late-type stars are most likely bound to the Milky Way. Only one late-type object (LAMOST J115209.12+120258.0) is unbound from the Galaxy. Performing integrations of orbital histories, we find that this object cannot have been ejected from the Galactic centre and thus may be either debris from the disruption of a satellite galaxy or a disc runaway.
We study the distribution of angular positions and angular separations of unbound hypervelocity stars (HVSs). HVSs are spatially anisotropic at the 3-sigma level. The spatial anisotropy is significant in Galactic longitude, not in latitude, and the inclusion of lower velocity, possibly bound HVSs reduces the significance of the anisotropy. We discuss how the observed distribution of HVSs may be linked to their origin. In the future, measuring the distribution of HVSs in the southern sky will provide additional constraints on the spatial anisotropy and the origin of HVSs.
We develop a novel method to simultaneously determine the vertical potential, force and stellar $z-v_z$ phase space distribution function (DF) in our local patch of the Galaxy. We assume that the Solar Neighborhood can be treated as a one-dimensional system in dynamical equilibrium and directly fit the number density in the $z-v_z$ plane to what we call the Rational Linear DF (RLDF) model. This model can be regarded as a continuous sum of isothermal DFs though it has only one more parameter than the isothermal model. We apply our method to a sample of giant stars from Gaia Data Release 2 and show that the RLDF provides an excellent fit to the data. The well-known phase space spiral emerges in the residual map of the $z-v_z$ plane. We use the best-fit potential to plot the residuals in terms of the frequency and angle of vertical oscillations and show that the spiral maps into a straight line. From its slope, we estimate that the phase spirals were generated by a perturbation $sim540$ Myr years ago. We also determine the differential surface density as a function of vertical velocity dispersion, a.k.a. the vertical temperature distribution. The result is qualitatively similar to what was previously found for SDSS/SEGUE G dwarfs. Finally, we address parameter degeneracies and the validity of the 1D approximation. Particularly, the mid-plane density derived from a cold subsample, where the 1D approximation is more secure, is closer to literature values than that derived from the sample as a whole.
We analyse N-body and Smoothed Particle Hydrodynamic (SPH) simulations of young star-forming regions to search for differences in the spatial distributions of massive stars compared to lower-mass stars. The competitive accretion theory of massive star formation posits that the most massive stars should sit in deeper potential wells than lower-mass stars. This may be observable in the relative surface density or spatial concentration of the most massive stars compared to other, lower-mass stars. Massive stars in cool--collapse N-body models do end up in significantly deeper potentials, and are mass segregated. However, in models of warm (expanding) star-forming regions, whilst the massive stars do come to be in deeper potentials than average stars, they are not mass segregated. In the purely hydrodynamical SPH simulations, the massive stars do come to reside in deeper potentials, which is due to their runaway growth. However, when photoionisation and stellar winds are implemented in the simulations, these feedback mechanisms regulate the mass of the stars and disrupt the inflow of gas into the clouds potential wells. This generally makes the potential wells shallower than in the control runs, and prevents the massive stars from occupying deeper potentials. This in turn results in the most massive stars having a very similar spatial concentration and surface density distribution to lower-mass stars. Whilst massive stars do form via competitive accretion in our simulations, this rarely translates to a different spatial distribution and so any lack of primordial mass segregation in an observed star-forming region does not preclude competitive accretion as a viable formation mechanism for massive stars.
The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in Astronomy. It includes precise astrometric data (positions, proper motions and parallaxes) for more than $1.3$ billion sources, mostly stars. To analyse such a vast amount of new data, the use of data mining techniques and machine learning algorithms are mandatory. The search for Open Clusters, groups of stars that were born and move together, located in the disk, is a great example for the application of these techniques. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. We explore the performance of a density based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an Artificial Neural Network (ANN) to automatically distinguish between real Open Clusters and statistical clusters. The development and implementation of this method to a $5$-Dimensional space ($l$, $b$, $varpi$, $mu_{alpha^*}$, $mu_delta$) to the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby Open Clusters. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
