Spectroscopy of Giant Stars in the Pyxis Globular Cluster

The Pyxis globular cluster is a recently discovered globular cluster that lies in the outer halo (R_{gc} ~ 40 kpc) of the Milky Way. Pyxis lies along one of the proposed orbital planes of the Large Magellanic Cloud (LMC), and it has been proposed to be a detached LMC globular cluster captured by the Milky Way. We present the first measurement of the radial velocity of the Pyxis globular cluster based on spectra of six Pyxis giant stars. The mean heliocentric radial velocity is ~ 36 km/sec, and the corresponding velocity of Pyxis with respect to a stationary observer at the position of the Sun is ~ -191 km/sec. This radial velocity is a large enough fraction of the cluster's expected total space velocity, assuming that it is bound to the Milky Way, that it allows strict limits to be placed on the range of permissible transverse velocities that Pyxis could have in the case that it still shares or nearly shares an orbital pole with the LMC. We can rule out that Pyxis is on a near circular orbit if it is Magellanic debris, but we cannot rule out an eccentric orbit associated with the LMC. We have calculated the range of allowed proper motions for the Pyxis globular cluster that result in the cluster having an orbital pole within 15 degrees of the present orbital pole of the LMC and that are consistent with our measured radial velocity, but verification of the tidal capture hypothesis must await proper motion measurement from the Space Interferometry Mission or HST. A spectroscopic metallicity estimate of [Fe/H] = -1.4 +/- 0.1 is determined for Pyxis from several spectra of its brightest giant; this is consistent with photometric determinations of the cluster metallicity from isochrone fitting.Comment: 22 pages, 5 figures, aaspp4 style, accepted for publication in October, 2000 issue of the PAS

Semi-automatic assessment of I/O behavior by inspecting the individual client-node timelines— an explorative study on 10^6 jobs

HPC applications with suboptimal I/O behavior interfere with well-behaving applications and lead to increased application runtime. In some cases, this may even lead to unresponsive systems and unfinished jobs. HPC monitoring systems can aid users and support staff to identify problematic behavior and support optimization of problematic applications. The key issue is how to identify relevant applications? A profile of an application doesn’t allow to identify problematic phases during the execution but tracing of each individual I/O is too invasive. In this work, we split the execution into segments, i.e., windows of fixed size and analyze profiles of them. We develop three I/O metrics to identify three relevant classes of inefficient I/O behaviors, and evaluate them on raw data of 1,000,000 jobs on the supercomputer Mistral. The advantages of our method is that temporal information about I/O activities during job runtime is preserved to some extent and can be used to identify phases of inefficient I/O. The main contribution of this work is the segmentation of time series and computation of metrics (Job-I/O-Utilization, Job-I/O-Problem-Time, and Job-I/O-Balance) that are effective to identify problematic I/O phases and jobs

Continuous, Semi-discrete, and Fully Discretized Navier-Stokes Equations

The Navier--Stokes equations are commonly used to model and to simulate flow phenomena. We introduce the basic equations and discuss the standard methods for the spatial and temporal discretization. We analyse the semi-discrete equations -- a semi-explicit nonlinear DAE -- in terms of the strangeness index and quantify the numerical difficulties in the fully discrete schemes, that are induced by the strangeness of the system. By analyzing the Kronecker index of the difference-algebraic equations, that represent commonly and successfully used time stepping schemes for the Navier--Stokes equations, we show that those time-integration schemes factually remove the strangeness. The theoretical considerations are backed and illustrated by numerical examples.Comment: 28 pages, 2 figure, code available under DOI: 10.5281/zenodo.998909, https://doi.org/10.5281/zenodo.99890

Magellanic Cloud Periphery Carbon Stars IV: The SMC

The kinematics of 150 carbon stars observed at moderate dispersion on the periphery of the Small Magellanic Cloud are compared with the motions of neutral hydrogen and early type stars in the Inter-Cloud region. The distribution of radial velocities implies a configuration of these stars as a sheet inclined at 73+/-4 degrees to the plane of the sky. The near side, to the South, is dominated by a stellar component; to the North, the far side contains fewer carbon stars, and is dominated by the neutral gas. The upper velocity envelope of the stars is closely the same as that of the gas. This configuration is shown to be consistent with the known extension of the SMC along the line of sight, and is attributed to a tidally induced disruption of the SMC that originated in a close encounter with the LMC some 0.3 to 0.4 Gyr ago. The dearth of gas on the near side of the sheet is attributed to ablation processes akin to those inferred by Weiner & Williams (1996) to collisional excitation of the leading edges of Magellanic Stream clouds. Comparison with pre LMC/SMC encounter kinematic data of Hardy, Suntzeff, & Azzopardi (1989) of carbon stars, with data of stars formed after the encounter, of Maurice et al. (1989), and Mathewson et al. (a986, 1988) leaves little doubt that forces other than gravity play a role in the dynamics of the H I.Comment: 30 pages; 7 figures, latex compiled, 1 table; to appear in AJ (June 2000

Assessing Deep Generative Models in Chemical Composition Space

The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table since even a single-crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on reinforcement learning, variational autoencoders, and generative adversarial networks for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2 million main-group Elpasolites, the precision, coverage, and diversity of the generated materials are rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed