Vaex: Big Data exploration in the era of Gaia

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: https://vaex.io, https://docs.vaex.io or https://github.com/maartenbreddels/vaexComment: 14 pages, 8 figures, Submitted to A&A, interactive version of Fig 4: https://vaex.io/paper/fig

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 Gaia DR2 data. 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 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", which traces the metal-poor halo main sequence reported by Gaia Collaboration et al. (2018d). We also find many small clumps especially noticeable in the tails of the velocity distribution of the stars in our sample. Their HR diagrams disclose narrow sequences characteristic of simple stellar populations. This stream-frosting confirms predictions from cosmological simulations, namely that substructure is most apparent amongst the fastest moving stars, typically reflecting more recent accretion events.Comment: 5 pages, 5 figures, accepted for publication in ApJ

Globular clusters in the Local Group as probes of galaxy assembly

Understanding the formation and evolution of galaxies is one of the most active areas of research in astrophysics. Hierarchical merging of proto-galactic fragments to build more massive galaxies is the current preferred model. A key prediction of this theory is that haloes of nearby galaxies should contain remnants of this assembly process in the form of tidal debris. Found in all but the smallest of dwarf galaxies, globular clusters (GC) are excellent probes of galaxy haloes. Having high luminosities, they are favourable targets in the outer regions of galaxies where the associated stellar surface brightness is low. GCs are thought to be amongst the oldest stellar systems in the Universe, and are likely born in the most significant phases of galaxy formation. Their metallicities, ages, spatial distributions and kinematics can be used to constrain the assembly history of their host galaxy. In this thesis, I explore the photometric and kinematic properties of several GC systems in our cosmological backyard, the Local Group of galaxies. The work is based on a major spectroscopic campaign, follow-up to the photometric Pan- Andromeda Archaeological Survey (PAndAS), as well as additional optical and near-IR data sets. Radial velocities are obtained for 78 GCs in the halo ofM31, 63 of which had no previous spectroscopic information. The GCs have projected radii between ∼ 20 and 140 kpc, thus sampling the true outer halo of this galaxy. In addition, GCs in the dwarf galaxies NGC 147, NGC 185 and NGC 6822 are also spectroscopically observed. By conducting a detailed kinematic analysis, I find that GCs in the outer halo of M31 exhibit coherent rotation around the minor optical axis, in the same direction as their more centrally located counterparts, but with a smaller amplitude of 86 ± 17 km s−1. There is also evidence that the velocity dispersion of the outer halo GC system decreases as a function of projected radius from theM31 centre, and this relation can be well described by a power lawof index ≈ −0.5. I detect and discuss various velocity correlations amongst GCs that lie on stellar streams in the M31 halo. Simple Monte Carlo tests show that such configurations are unlikely to form by chance, implying that significant fraction of the GCs in the M31 halo have been accreted alongside their parent dwarf galaxies. I also estimate the dynamical mass of M31 within 200 kpc to be (1.2 − 1.6) ± 0.2 × 1012 M⊙. I also characterize the GC systems of three dwarf galaxies in the Local Group: the dwarf elliptical satellites of M31, NGC 147 and NGC 185, and the isolated dwarf irregular NGC 6822. Using uniform optical and near-IR photometry, I constrain the ages and metallicities of their constituent GCs. The metallicities of the GCs around NGC 147 and NGC 185 are found to be metal-poor ([Fe/H]. −1.25 dex), while their ages are more difficult to constrain. The GCs hosted by NGC 6822 are found to be old (>9 Gyr) and to have a spread of metallicities (−1.6 . [Fe/H] . −0.4). I find close similarity between the mean optical (V − I)0 colours of the GCs hosted by these three dwarf galaxies to those located in the M31 outer halo, consistent with the idea that dwarf galaxies akin to them might have contributed toward the assembly of the M31 outer halo GC population. Analysing their kinematics, I find no evidence for systemic rotation in either of these three GC systems. Finally, I use the available GC kinematic data to calculate the dynamical masses of NGC 147, NGC 185 and NGC 6822

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 Way's 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 73%) 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.Comment: 19 pages, 16 figures. A&A in pres

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

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.Comment: Accepted for publication in Astronomy & Astrophysics. 13 pages, 9 figure, 2 table

Unveiling the stellar halo with TGAS

The detailed study of the Galactic stellar halo may hold the key to unlocking the assembly history of the Milky Way. Here, we present a machine learning model for selecting metal poor stars from the TGAS catalogue using 5 dimensional phase-space information, coupled with optical and near-IR photometry. We characterise the degree of substructure in our halo sample in the Solar neighbourhood by measuring the velocity correlation function.<br/

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

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