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Context. Extended stellar haloes are a natural by-product of the hierarchical formation of massive galaxies like the Milky Way. If merging is a non-negligible factor in the growth of our Galaxy, evidence of such events should be encoded in its stellar halo. The reliable identification of genuine halo stars is a challenging task, however. Aims. With the advent of the Gaia space telescope, we are ushered into a new era of Galactic astronomy. The first Gaia data release contains the positions, parallaxes, and proper motions for over two million stars, mostly in the solar neighbourhood. The second Gaia data release will enlarge this sample to over 1.5 billion stars, the brightest ~ 5 million of which will have full phase-space information. Our aim for this paper is to develop a machine learning model for reliably identifying halo stars, even when their full phase-space information is not available. Methods. We use the Gradient Boosted Trees algorithm to build a supervised halo star classifier. The classifier is trained on a sample of stars extracted from the Gaia Universe Model Snapshot, which is also convolved with the errors of the public TGAS data, which is a subset of Gaia DR1, as well as with the expected uncertainties for the upcoming Gaia DR2 catalogue. We also trained our classifier on a dataset resulting from the cross-match between the TGAS and RAVE catalogues, where the halo stars are labelled in an entirely model-independent way. We then use this model to identify halo stars in TGAS. Results. When full phase-space information is available and for Gaia DR2-like uncertainties, our classifier is able to recover 90% of the halo stars with at most 30% distance errors, in a completely unseen test set and with negligible levels of contamination. When line-of-sight velocity is not available, we recover ~ 60% of such halo stars, with less than 10% contamination. When applied to the TGAS catalogue, our classifier detects 337 high confidence red giant branch halo stars. At first glance this number may seem small, however, it is consistent with the expectation from the models, given the uncertainties in the data. The large parallax errors are in fact the biggest limitation in our ability to identify a large number of halo stars in all the cases studied. [ABSTRACT FROM AUTHOR]

Aims. We study the dynamical properties of halo stars located in the solar neighbourhood. Our goal is to explore how the properties of the halo depend on the selection criteria used to define a sample of halo stars. Once this is understood, we proceed to measure the shape and orientation of the halo’s velocity ellipsoid and we use this information to put constraints on the gravitational potential of the Galaxy. Methods. We use the recently released Gaia DR1 catalogue cross-matched to the RAVE dataset for our analysis.We develop a dynamical criterion based on the distribution function of stars in various Galactic components, using action integrals to identify halo members, and we compare this to the metallicity and to kinematically selected samples. Results. With this new method, we find 1156 stars in the solar neighbourhood that are likely members of the stellar halo. Our dynamically selected sample consists mainly of distant giants on elongated orbits. Their metallicity distribution is rather broad, with roughly half of the stars having [M/H] ≥ -1 dex. The use of different selection criteria has an important impact on the characteristics of the velocity distributions obtained. Nonetheless, for our dynamically selected and for the metallicity selected samples, we find the local velocity ellipsoid to be aligned in spherical coordinates in a Galactocentric reference frame. This suggests that the total gravitational potential is rather spherical in the region spanned by the orbits of the halo stars in these samples. [ABSTRACT FROM AUTHOR]

We present a new discrete chemo-dynamical axisymmetric modelling technique, which we apply to the dwarf spheroidal galaxy Sculptor. The major improvement over previous Jeans models is that realistic chemical distributions are included directly in the dynamical modelling of the discrete data. This avoids loss of information due to spatial binning and eliminates the need for hard cuts to remove contaminants and to separate stars based on their chemical properties. Using a combined likelihood in position, metallicity and kinematics, we find that our models naturally separate Sculptor stars into a metal-rich and a metal-poor population. Allowing for non-spherical symmetry, our approach provides a central slope of the dark matter density of γ = 0.5 ± 0.3. The metal-rich population is nearly isotropic (with βredr=0.0±0.1), while the metal-poor population is tangentially anisotropic (with βbluer=−0.2±0.1) around the half-light radius of 0.26 kpc. A weak internal rotation of the metal-rich population is revealed with vmax/σ0 = 0.15 ± 0.15. We run tests using mock data to show that a discrete data set with ∼6000 stars is required to distinguish between a core (γ = 0) and cusp (γ = 1), and to constrain the possible internal rotation to better than 1σ confidence with our model. We conclude that our discrete chemo-dynamical modelling technique provides a flexible and powerful tool to robustly constrain the internal dynamics of multiple populations, and the total mass distribution in a stellar system. [ABSTRACT FROM AUTHOR]

The thermal stability of a weakly magnetized, rotating, stratified, optically thin plasma is studied by means of linear-perturbation analysis. We derive dispersion relations and criteria for stability against axisymmetric perturbations that generalize previous results on either nonrotating or unmagnetized fluids. The implications for the hot atmospheres of galaxies and galaxy clusters are discussed. [ABSTRACT FROM AUTHOR]