Jovan Veljanoski

Gaia Data Release 1 - Catalogue validation

Before the publication of the Gaia Catalogue, the contents of the first data release have undergone multiple dedicated validation tests. These tests aim at analysing in-depth the Catalogue content to detect anomalies, individual problems in specific objects or in overall statistical properties, either to filter them before the public release, or to describe the different caveats of the release for an optimal exploitation of the data. Dedicated methods using either Gaia internal data, external catalogues or models have been developed for the validation processes. They are testing normal stars as well as various populations like open or globular clusters, double stars, variable stars, quasars. Properties of coverage, accuracy and precision of the data are provided by the numerous tests presented here and jointly analysed to assess the data release content. This independent validation confirms the quality of the published data, Gaia DR1 being the most precise all-sky astrometric and photometric catalogue to-date. However, several limitations in terms of completeness, astrometric and photometric quality are identified and described. Figures describing the relevant properties of the release are shown and the testing activities carried out validating the user interfaces are also described. A particular emphasis is made on the statistical use of the data in scientific exploitation.

Gaia Data Release 2: Catalogue validation

Context. The second Gaia data release (DR2) contains very precise astrometric and photometric properties for more than one billion sources, astrophysical parameters for dozens of millions, radial velocities for millions, variability information for half a million stars from selected variability classes, and orbits for thousands of solar system objects. nAims: Before the catalogue was published, these data have undergone dedicated validation processes. The goal of this paper is to describe the validation results in terms of completeness, accuracy, and precision of the various Gaia DR2 data. nMethods: The validation processes include a systematic analysis of the catalogue content to detect anomalies, either individual errors or statistical properties, using statistical analysis and comparisons to external data or to models. nResults: Although the astrometric, photometric, and spectroscopic data are of unprecedented quality and quantity, it is shown that the data cannot be used without dedicated attention to the limitations described here, in the catalogue documentation and in accompanying papers. We place special emphasis on the caveats for the statistical use of the data in scientific exploitation. In particular, we discuss the quality filters and the consideration of the properties, systematics, and uncertainties from astrometry to astrophysical parameters, together with the various selection functions.

A box full of chocolates: The rich structure of the nearby stellar halo revealed by Gaia and RAVE

The hierarchical structure formation model predicts that stellar halos should form, at least partly, via mergers. If this was a predominant formation channel for the Milky Ways halo, imprints of this merger history in the form of moving groups or streams should exist also in the vicinity of the Sun. Here we study the kinematics of halo stars in the Solar neighbourhood using the very recent first data release from the Gaia mission, and in particular the TGAS dataset, in combination with data from the RAVE survey. Our aim is to determine the amount of substructure present in the phase-space distribution of halo stars that could be linked to merger debris. To characterise kinematic substructure, we measure the velocity correlation function in our sample of halo (low metallicity) stars. We also study the distribution of these stars in the space of energy and two components of the angular momentum, in what we call Integrals of Motion space. The velocity correlation function reveals substructure in the form of an excess of pairs of stars with similar velocities, well above that expected for a smooth distribution. Comparison to cosmological simulations of the formation of stellar halos indicate that the levels found are consistent with the Galactic halo having been built fully via accretion. Similarly, the distribution of stars in the space of Integrals of motion is highly complex. A strikingly high fraction (between 58% and upto 84%) of the stars that are somewhat less bound than the Sun are on (highly) retrograde orbits. A simple comparison to Milky Way-mass galaxies in cosmological hydrodynamical simulations suggests that less than 1% have such prominently retrograde outer halos. We also identify several other statistically significant structures in Integrals of Motion space that could potentially be related to merger events.

One Large Blob and Many Streams Frosting the nearby Stellar Halo in Gaia~DR2

We explore the phase-space structure of nearby halo stars identified kinematically from the Gaia second data release (DR2). We focus on their distribution in velocity and in integrals of motion space, as well as on their photometric properties. Our sample of stars selected to be moving at a relative velocity of at least 210 km s−1, with respect to the Local Standard of Rest, contains an important contribution from the low rotational velocity tail of the disk(s). The V R -distribution of these stars depicts a small asymmetry similar to that seen for the faster rotating thin disk stars near the Sun. We also identify a prominent, slightly retrograde blob that traces the metal-poor halo main sequence reported by Gaia Collaboration et al. We also find many small clumps that are especially noticeable in the tails of the velocity distribution of the stars in our sample. Their Hertzsprung–Russell (HR) diagrams disclose narrow sequences characteristic of simple stellar populations. This stream-frosting confirms predictions from cosmological simulations, namely that substructure is most apparent among the fastest moving stars, typically reflecting more recent accretion events.

The dynamically selected stellar halo of the Galaxy with Gaia and the tilt of the velocity ellipsoid

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 for defining a sample of halo stars. Once this is understood we proceed to measure the shape and orientation of the halos velocity ellipsoid and we use this information to put constraints on the gravitational potential of the Galaxy. 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 compare this to metallicity and to kinematically selected samples. With this new method, we find 1156 stars in the Solar Neighbourhood to be 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]

Vaex: big data exploration in the era of Gaia

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Leafs on trees: identifying halo stars with extreme gradient boosted trees

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.

The kinematics of globular clusters systems in the outer halos of the Aquarius simulations

We present a new Python library called vaex, to handle extremely large tabular datasets, such as astronomical catalogues like the Gaia catalogue, N-body simulations or any other regular datasets which can be structured in rows and columns. Fast computations of statistics on regular N-dimensional grids allows analysis and visualization in the order of a billion rows per second. We use streaming algorithms, memory mapped files and a zero memory copy policy to allow exploration of datasets larger than memory, e.g. out-of-core algorithms. Vaex allows arbitrary (mathematical) transformations using normal Python expressions and (a subset of) numpy functions which are lazily evaluated and computed when needed in small chunks, which avoids wasting of RAM. Boolean expressions (which are also lazily evaluated) can be used to explore subsets of the data, which we call selections. Vaex uses a similar DataFrame API as Pandas, a very popular library, which helps migration from Pandas. Visualization is one of the key points of vaex, and is done using binned statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colormapping) and 3d (using volume rendering). Vaex is split in in several packages: vaex-core for the computational part, vaex-viz for visualization mostly based on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based in IPyWidgets, vaex-server for the (optional) client-server communication, vaex-ui for the Qt based interface, vaex-hdf5 for hdf5 based memory mapped storage, vaex-astro for astronomy related selections, transformations and memory mapped (column based) fits storage. Vaex is open source and available under MIT license on github, documentation and other information can be found on the main website: this https URL, this https URL or this https URL

The merger that led to the formation of the Milky Way’s inner stellar halo and thick disk

Extended stellar haloes are a natural by-product of the hierarchical formation of massive galaxies. If merging is a non-negligible factor in the growth of our Galaxy, evidence of such events should be encoded in its stellar halo. Reliable identification of genuine halo stars is a challenging task however. The 1st Gaia data release contains the positions, parallaxes and proper motions for over 2 million stars, mostly in the Solar neighbourhood. Gaia DR2 will enlarge this sample to over 1.5 billion stars, the brightest ~5 million of which will have a full phase-space information. Our aim is to develop a machine learning model to reliably identify halo stars, even when their full phase-space information is not available. We use the Gradient Boosted Trees algorithm to build a supervised halo star classifier. The classifier is trained on a sample extracted from the Gaia Universe Model Snapshot, convolved with the errors of TGAS, as well as with the expected uncertainties of the upcoming Gaia DR2. We also trained our classifier on 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. 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 data, our classifier detects 337 high confidence RGB halo stars. Although small, this number is consistent with the expectation from models given the data uncertainties. The large parallax errors are the biggest limitation to identify a larger number of halo stars in all the cases studied.

The formation of two of the major structural components of the Milky Way

Stellar halos and globular cluster (GC) systems contain valuable information regarding the assembly history of their host galaxies. Motivated by the detection of a significant rotation signal in the outer halo GC system of M31, we investigate the likelihood of detecting such a rotation signal in projection, using cosmological simulations. To this end we select subsets of tagged particles in the halos of the Aquarius simulations to represent mock GC systems, and analyse their kinematics. We find that GC systems can exhibit a non-negligible rotation signal provided the associated stellar halo also has a net angular momentum. The ability to detect this rotation signal is highly dependent on the viewing perspective, and the probability of seeing a signal larger than that measured in M 31 ranges from 10% to 90% for the different halos in the Aquarius suite. High values are found from a perspective such that the projected angular momentum of the GC system is within less than or similar to 40 deg of the rotation axis determined via the projected positions and line-of-sight velocities of the GCs. Furthermore, the true 3D angular momentum of the outer stellar halo is relatively well aligned, within 35 deg, with that of the mock GC systems. We argue that the net angular momentum in the mock GC systems arises naturally when the majority of the material is accreted from a preferred direction, namely along the dominant dark matter filament of the large-scale structure that the halos are embedded in. This, together with the favourable edge-on view of M 31s disk suggests that it is not a coincidence that a large rotation signal has been measured for its outer halo GC system.