23 research outputs found
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
-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