Numerical Investigation of Hypersonic Unsteady Flow Around a Spiked Blunt-body

AbstractThe obvious unsteady flow characteristics of the flow field around the spiked blunt-body have negative effect on the drag reduction of vehicle and the thermo-protection of the head. The hypersonic self-sustained oscillatory flow was numerical simulated, and the flow structure and mechanism of the three process of one cycle: collapse, inflation and withhold, were discussed in detail. The correspondence between the drag coefficient curve and the different flow structure was obtained, which will provide basis to the drag reduction and thermo-protection of hypersonic blunt-body vehicle through flow control in the future

Robust Clustering of the Local Milky Way Stellar Kinematic Substructures with Gaia eDR3

We apply the clustering algorithm HDBSCAN on the Gaia early third data release astrometry combined with the Gaia second data release radial velocity measurements of almost 5.5 million stars to identify the local stellar kinematic substructures in the solar neighborhood. Understanding these structures helps build a more complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. The main goal of this study is to provide a list of the most stable clusters, by taking into account the measurement uncertainties and studying the stability of the clustering results. We apply the clustering algorithm in two spaces, in velocity space in order to study recently accreted structures, and in action-angle space to find phase-mixed structures. We find 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disk (Sequoia) are identified in velocity space (action-angle space). We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the most stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries. (abridged)Comment: 35 pages, 16 figures. Comments are welcom

Synthetic Gaia DR3 surveys from the FIRE cosmological simulations of Milky-Way-mass galaxies

The third data release (DR3) of Gaia has provided a five-fold increase in the number of radial velocity measurements of stars, as well as a stark improvement in parallax and proper motion measurements. To help with studies that seek to test models and interpret Gaia DR3, we present nine Gaia synthetic surveys, based on three solar positions in three Milky-Way-mass galaxies of the Latte suite of the Fire-2 cosmological simulations. These synthetic surveys match the selection function, radial velocity measurements, and photometry of Gaia DR3, adapting the code base Ananke, previously used to match the Gaia DR2 release in Sanderson et al. 2020. The synthetic surveys are publicly available and can be found at http://ananke.hub.yt/. Similarly to the previous release of Ananke, these surveys are based on cosmological simulations and thus able to model non-equilibrium dynamical effects, making them a useful tool in testing and interpreting Gaia DR3.Comment: 17 pages, 7 tables, 6 figure

Stellar Metallicities from SkyMapper Photometry II: Precise photometric metallicities of ∼\sim280,000 giant stars with [Fe/H] <−0.75< -0.75 in the Milky Way

The Milky Way's metal-poor stars are nearby ancient objects that are used to study early chemical evolution and the assembly and structure of the Milky Way. Here we present reliable metallicities of $\sim280,000$ stars with $-3.75 \lesssim$ [Fe/H] $\lesssim -0.75$ down to $g=17$ derived using metallicity-sensitive photometry from the second data release (DR2) of the SkyMapper Southern Survey. We use the dependency of the flux through the SkyMapper $v$ filter on the strength of the Ca II K absorption features, in tandem with SkyMapper $u,g,i$ photometry, to derive photometric metallicities for these stars. We find that metallicities derived in this way compare well to metallicities derived in large-scale spectroscopic surveys, and use such comparisons to calibrate and quantify systematics as a function of location, reddening, and color. We find good agreement with metallicities from the APOGEE, LAMOST, and GALAH surveys, based on a standard deviation of $\sigma\sim0.25$dex of the residuals of our photometric metallicities with respect to metallicities from those surveys. We also compare our derived photometric metallicities to metallicities presented in a number of high-resolution spectroscopic studies to validate the low metallicity end ([Fe/H] $< -2.5$) of our photometric metallicity determinations. In such comparisons, we find the metallicities of stars with photometric [Fe/H] $< -2.5$ in our catalog show no significant offset and a scatter of $\sigma\sim$0.31dex level relative to those in high-resolution work when considering the cooler stars ($g-i > 0.65$) in our sample. We also present an expanded catalog containing photometric metallicities of $\sim720,000$ stars as a data table for further exploration of the metal-poor Milky Way.Comment: 15 pages, 9 figures, 2 tables; submitted to ApJS and revised after one round of referee feedback. Full version of Table 2 in sourc

Anapole mediated giant photothermal nonlinearity in nanostructured silicon

Featured with a plethora of electric and magnetic Mie resonances, high index dielectric nanostructures offer a versatile platform to concentrate light-matter interactions at the nanoscale. By integrating unique features of far-field scattering control and near-field concentration from radiationless anapole states, here, we demonstrate a giant photothermal nonlinearity in single subwavelength-sized silicon nanodisks. The nanoscale energy concentration and consequent near-field enhancements mediated by the anapole mode yield a reversible nonlinear scattering with a large modulation depth and a broad dynamic range, unveiling a record-high nonlinear index change up to 0.5 at mild incident light intensities on the order of MW/cm2. The observed photothermal nonlinearity showcases three orders of magnitude enhancement compared with that of unstructured bulk silicon, as well as nearly one order of magnitude higher than that through the radiative electric dipolar mode. Such nonlinear scattering can empower distinctive point spread functions in confocal reflectance imaging, offering the potential for far-field localization of nanostructured Si with an accuracy approaching 40 nm. Our findings shed new light on active silicon photonics based on optical anapoles

Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy

Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge to select the reparameterization operations. However, the performance of architecture is limited by the type of operations and prior knowledge. To break this restriction, in this work, an improved re-parameterization search space is designed, which including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further improved by the search space. To effectively explore this search space, an automatic re-parameterization enhancement strategy is designed based on neural architecture search (NAS), which can search a excellent re-parameterization architecture. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k.Comment: 21pages, 8figure

An automatic solution to make HTCondor more stable and easier

HTCondor has been widely adopted by HEP clusters to provide high-level scheduling performance. Unlike other schedulers, HTCondor provides loose management of the worker nodes. We developed a maintenance automation tool called “HTCondor MAT” that focuses on dynamic resource management and automatic error handling. A central database records all worker node information, which is sent to the worker node for the startd configuration. If an error happens for the worker node, the node information stored in the database is updated and the worker node is reconfigured with the new node information. The new configuration stops the startd from accepting error-related jobs until the worker node recovers. The MAT has been deployed in the IHEP HTC cluster to provide a central way to manage the worker nodes and remove the impacts of errors on the worker nodes automatically

An automatic solution to make HTCondor more stable and easier

HTCondor has been widely adopted by HEP clusters to provide high-level scheduling performance. Unlike other schedulers, HTCondor provides loose management of the worker nodes. We developed a maintenance automation tool called “HTCondor MAT” that focuses on dynamic resource management and automatic error handling. A central database records all worker node information, which is sent to the worker node for the startd configuration. If an error happens for the worker node, the node information stored in the database is updated and the worker node is reconfigured with the new node information. The new configuration stops the startd from accepting error-related jobs until the worker node recovers. The MAT has been deployed in the IHEP HTC cluster to provide a central way to manage the worker nodes and remove the impacts of errors on the worker nodes automatically