50 Search Results

ABSTRACT In the Gaia era, stellar kinematics are extensively used to study Galactic halo stellar populations, to search for halo structures, and to characterize the interface between the halo and hot disc populations. We use distribution function-based models of modern data sets with 6D phase space data to qualitatively describe a variety of kinematic spaces commonly used in the study of the Galactic halo. Furthermore, we quantitatively assess how well each kinematic space can separate radially anisotropic from isotropic halo populations. We find that scaled action space (the ‘action diamond’) is superior to other commonly used kinematic spaces at thismore » task. We present a new, easy to implement selection criterion for members of the radially anisotropic Gaia–Enceladus merger remnant. Assuming a 1:1 ratio of Gaia–Enceladus stars to more isotropic halo, we find our selection achieves a sample purity of 86 per cent in our models with respect to contamination from the more isotropic halo. We compare this criterion to literature criteria, finding that it produces the highest purity in the resulting samples, at the expense of a modest reduction in completeness. We also show that selection biases that underlie nearly all contemporary spectroscopic data sets can noticeably impact the E−Lz distribution of samples in a manner that may be confused for real substructure. We conclude by providing recommendations for how authors should use stellar kinematics in the future to study the Galactic stellar halo.« less

ABSTRACT The way supermassive black holes (SMBHs) in Galactic Centres (GCs) accumulate their mass is not completely determined. At large scales, it is governed by galactic encounters, mass inflows connected to spirals arms and bars, or due to expanding shells from supernova (SN) explosions in the central parts of galaxies. The investigation of the latter process requires an extensive set of gas dynamical simulations to explore the multidimensional parameter space needed to frame the phenomenon. The aims of this paper are to extend our investigation of the importance of SNe for inducing accretion on to an SMBH and carry outmore » a comparison between the fully hydrodynamic code flash and the much less computationally intensive code ring, which uses the thin shell approximation. We simulate 3D expanding shells in a gravitational potential similar to that of the GC with a variety of homogeneous and turbulent environments. In homogeneous media, we find convincing agreement between flash and ring in the shapes of shells and their equivalent radii throughout their whole evolution until they become subsonic. In highly inhomogeneous, turbulent media, there is also a good agreement of shapes and sizes of shells, and of the times of their first contact with the central 1-pc sphere, where we assume that they join the accretion flow. The comparison supports the proposition that an SN occurring at a galactocentric distance of 5 pc typically drives 1–3 M⊙ into the central 1 pc around the GC.« less

APOGEE and GALAH are two high resolution multi-object spectroscopic surveys that provide fundamental stellar parameters and multiple elemental abundance estimates for about half a million stars in the Milky Way. Both surveys observe in different wavelength regimes and use different data reduction pipelines leading to significant offsets and trends in stellar parameters and abundances for the common stars observed in both surveys. Such systematic differences/offsets in stellar parameters and abundances make it difficult to effectively utilize them to investigate Galactic abundance trends in spite of the unique advantage provided by their complementary sky coverage and different Milky Way components theymore » observe. Hence, we use the Cannon data-driven method selecting a training set of 4418 common stars observed by both surveys. This enables the construction of two catalogues, one with the APOGEE-scaled and the other with the GALAH-scaled stellar parameters. Using repeat observations in APOGEE and GALAH, we find high precision in metallicity (~0.02–0.4 dex) and alpha abundances (~0.02–0.03 dex) for spectra with good signal-to-noise ratio (SNR >; 80 for APOGEE and SNR > 40 for GALAH). We use open and globular clusters to validate our parameter estimates and find small scatter in metallicity (0.06 dex) and alpha abundances (0.03 dex) in APOGEE-scaled case. The final catalogues have been cross-matched with the Gaia EDR3 catalogue to enable their use to carry out detailed chemo-dynamic studies of the Milky Way from perspectives of APOGEE and GALAH.« less

ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search for line-like features inmore » patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.« less

ABSTRACT J01020100−7122208 is a star whose origin and nature still challenges us. It was first believed to be a yellow supergiant ejected from the Small Magellanic Cloud, but it was more recently claimed to be a red giant accelerated by the Milky Way’s central black hole. In order to unveil its nature, we analysed photometric, astrometric, and high-resolution spectroscopic observations to estimate the orbit, age, and 16 elemental abundances. Our results show that this star has a retrograde and highly-eccentric orbit, $$e=0.914_{-0.020}^{+0.016}$$. Correspondingly, it likely crossed the Galactic disc at 550 pc from the Galactic Centre. We obtained a spectroscopic massmore » and age of $$1.09\pm 0.10\, {\rm M}_\odot$$ and 4.51 ± 1.44 Gyr, respectively. Its chemical composition is similar to the abundance of other retrograde halo stars. We found that the star is enriched in europium, having [Eu/Fe] = 0.93 ± 0.24, and is more metal-poor than reported in the literature, with [Fe/H]  = −1.30 ± 0.10. This information was used to conclude that J01020100−7122208 is likely not a star ejected from the central black of the Milky Way or from the Small Magellanic Cloud. Instead, we propose that it is simply a halo star that was likely accreted by the Milky Way in the distant past, but its mass and age suggest it is probably an evolved blue straggler.« less

Here we combine proper motion data from Gaia EDR₃ and HST with line-of-sight velocity data to study the stellar kinematics of the ω Cen globular cluster. Using a steady-state, axisymmetric dynamical model, we measure the distribution of both the dark and luminous mass components. Assuming both Gaussian and Navarro–Frenk–White mass profiles, depending on the data set, we measure an integrated mass of ≲10⁶ M_⊙ within the ω Cen half-light radius for a dark component that is distinct from the luminous stellar component. For the HST and radial velocity data, models with a non-luminous mass component are strongly statistically preferred relative to a stellar mass-onlymore » model with a constant mass-to-light ratio. While a compact core of stellar remnants may account for a dynamical mass up to ~5 x 10⁵ M_⊙, they likely cannot explain the higher end of the range. This leaves open the possibility that this non-luminous dynamical mass component comprises non-baryonic dark matter. In comparison to the dark matter distributions around dwarf spheroidal galaxies, the ω Cen dark mass component is much more centrally concentrated. Interpreting the non-luminous mass distribution as particle dark matter, we use these results to obtain the J-factor, which sets the sensitivity to the annihilation cross-section. For the data sets considered, the range of median J-factors is ~10²²-10²⁴ GeV² cm^-5, which is larger than that obtained for any dwarf spheroidal galaxy.« less

ABSTRACT Using a sample of red giant stars from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16, we infer the conditional distribution $$p([\alpha /{\rm Fe}]\, |\, [{\rm Fe}/{\rm H}])$$ in the Milky Way disk for the α-elements Mg, O, Si, S, and Ca. In each bin of [Fe/H] and Galactocentric radius R, we model p([α/Fe]) as a sum of two Gaussians, representing ‘low-α’ and ‘high-α’ populations with scale heights $$z_1=0.45\, {\rm kpc}$$ and $$z_2=0.95\, {\rm kpc}$$, respectively. By accounting for age-dependent and z-dependent selection effects in APOGEE, we infer the [α/Fe] distributions that would be found formore » a fair sample of long-lived stars covering all z. Near the Solar circle, this distribution is bimodal at sub-solar [Fe/H], with the low-α and high-α peaks clearly separated by a minimum at intermediate [α/Fe]. In agreement with previous results, we find that the high-α population is more prominent at smaller R, lower [Fe/H], and larger |z|, and that the sequence separation is smaller for Si and Ca than for Mg, O, and S. We find significant intrinsic scatter in [α/Fe] at fixed [Fe/H] for both the low-α and high-α populations, typically ∼0.04-dex. The means, dispersions, and relative amplitudes of this two-Gaussian description, and the dependence of these parameters on R, [Fe/H], and α-element, provide a quantitative target for chemical evolution models and a test for hydrodynamic simulations of disk galaxy formation. We argue that explaining the observed bimodality will probably require one or more sharp transitions in the disk’s gas accretion, star formation, or outflow history in addition to radial mixing of stellar populations.« less

The characteristics of the stellar populations in the Galactic bulge inform and constrain the Milky Way’s formation and evolution. The metal-poor population is particularly important in light of cosmological simulations, which predict that some of the oldest stars in the Galaxy now reside in its centre. The metal-poor bulge appears to consist of multiple stellar populations that require dynamical analyses to disentangle. In this work, we undertake a detailed chemodynamical study of the metal-poor stars in the inner Galaxy. Using R ~ 20000 VLT/GIRAFFE spectra of 319 metal-poor (-2.55 dex ≤ [Fe/H] ≤ 0.83 dex, with $$\overline{[Fe/H]}$$ = -0.84 dex)more » stars, we perform stellar parameter analysis and report 12 elemental abundances (C, Na, Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Zn, Ba, and Ce) with precisions of ≈0.10 dex. Based on kinematic and spatial properties, we categorize the stars into four groups, associated with the following Galactic structures: the inner bulge, the outer bulge, the halo, and the disc. We find evidence that the inner and outer bulge population is more chemically complex (i.e. higher chemical dimensionality and less correlated abundances) than the halo population. This result suggests that the older bulge population was enriched by a larger diversity of nucleosynthetic events. We also find one inner bulge star with a [Ca/Mg] ratio consistent with theoretical pair-instability supernova yields and two stars that have chemistry consistent with globular cluster stars.« less

Zeolites, an important class of 3-dimensional nanoporous materials, have been widely explored for a variety of applications including gas storage, separations, and catalysis. As the properties of these aluminosilicate materials depend on a number of factors (e.g., framework topology, Si/Al ratio, extra-framework cations etc.), detailed experiments (e.g., catalytic properties, adsorption capacities etc.) are often limited to only a handful of materials. Computational methods have played an important role in (1) providing molecular level insights to rationalize experimental observations, and (2) screening large libraries of zeolites to identify promising candidates for experimental synthesis and validation. Different levels of theory and computationalmore » chemistry codes are necessary to describe the range of relevant phenomena such as adsorption (e.g., grand canonical Monte Carlo), diffusion (e.g., molecular dynamics), and chemical reactions (e.g., density functional theory). Manipulation of atomic structures, handling of input files, and developing robust workflows becomes quite cumbersome. To mitigate these challenges, we describe the development of the Multiscale Atomic Zeolite Simulation Environment (MAZE) – a Python package that simplifies zeolite-specific calculation workflows by providing a user-friendly interface for systematically manipulating zeolite structures« less

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This set-up provides 27 clusters created with this unsupervised learning that we show are well separated based on galaxy shape and structure (e.g. Sérsic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate wellmore » with physical properties such as the colour–magnitude diagram, and span the range of scaling relations such as mass versus size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $$\sim 87{{\ \rm per\ cent}}$$ is reached using an imbalanced data set, matching real galaxy distributions, which includes 22.7 per cent early-type galaxies and 77.3 per cent late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones.« less