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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Cold Fronts in Clusters of Galaxies: Observations and Modeling

Mergers of galaxy clusters -- some of the most energetic events in the Universe -- produce disturbances in hot intracluster medium, such as shocks and cold fronts, that can be used as tools to study the physics of galaxy clusters. Cold fronts may constrain viscosity and the structure and strength of the cluster magnetic fields. Combined with radio data, these observations also shed light on the production of ultrarelativistic particles that are known to coexist with the cluster thermal plasma. This talk will summarize the current X-ray observations of cluster mergers, as well as some recent radio data and high resolution hydrodynamic simulations.

Markevitch, Maxim↗
NASA NTRS2001

Efficiency of parallel direct optimization

Tremendous progress has been made at the level of sequential computation in phylogenetics. However, little attention has been paid to parallel computation. Parallel computing is particularly suited to phylogenetics because of the many ways large computational problems can be broken into parts that can be analyzed concurrently. In this paper, we investigate the scaling factors and efficiency of random addition and tree refinement strategies using the direct optimization software, POY, on a small (10 slave processors) and a large (256 slave processors) cluster of networked PCs running LINUX. These algorithms were tested on several data sets composed of DNA and morphology ranging from 40 to 500 taxa. Various algorithms in POY show fundamentally different properties within and between clusters. All algorithms are efficient on the small cluster for the 40-taxon data set. On the large cluster, multibuilding exhibits excellent parallel efficiency, whereas parallel building is inefficient. These results are independent of data set size. Branch swapping in parallel shows excellent speed-up for 16 slave processors on the large cluster. However, there is no appreciable speed-up for branch swapping with the further addition of slave processors (>16). This result is independent of data set size. Ratcheting in parallel is efficient with the addition of up to 32 processors in the large cluster. This result is independent of data set size. c2001 The Willi Hennig Society.

NASA Discipline Evolutionary Biology↗
DOE OSTI2012

CLaSPS: A NEW METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM COMPLEX ASTRONOMICAL DATA SETS

In this paper, we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex data sets, based on the application of distinct unsupervised clustering techniques. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. CLaSPS has been primarily developed as a tool to tackle the challenging complexity of the multi-wavelength complex and massive astronomical data sets produced by the federation of the data from modern automated astronomical facilities. In this paper, we discuss the applications of CLaSPS to two simple astronomical data sets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first data set, CSC+, is composed of optical quasars spectroscopically selected in the Sloan Digital Sky Survey data, observed in the x-rays by Chandra and with multi-wavelength observations in the near-infrared, optical, and ultraviolet spectral intervals. One of the results of the application of CLaSPS to the CSC+ is the re-identification of a well-known correlation between the {alpha}{sub OX} parameter and the near-ultraviolet color, in a subset of CSC+ sources with relatively small values of the near-ultraviolet colors. The other data set consists of a sample of blazars for which photometric observations in the optical, mid-, and near-infrared are available, complemented for a subset of the sources, by Fermi {gamma}-ray data. The main results of the application of CLaSPS to such data sets have been the discovery of a strong correlation between the multi-wavelength color distribution of blazars and their optical spectral classification in BL Lac objects and flat-spectrum radio quasars, and a peculiar pattern followed by blazars in the WISE mid-infrared colors space. This pattern and its physical interpretation have been discussed in detail in other papers by one of the authors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Data-driven analysis and prediction of wastewater treatment plant performance: Insights and forecasting for sustainable operations

Here this study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant's performance is conducted. The analysis involves independently assessing historical operational conditions, plant production, and their statistical correlations using various statistical techniques. The second objective focuses on developing a data-driven forecasting approach to predict the plant's production one month in advance, using multiple machine learning models. The results highlight the effectiveness of principal component analysis (PCA) in simplifying operational data, revealing distinct operational clusters, and identifying seasonal production patterns while showing correlations between operational conditions and overall power production. The support vector machine (SVM) forecasting model emerged as the top performer, showcasing the potential of a hybrid forecasting approach. The findings offer valuable perspectives for enhancing operational efficiency, refining production planning, and ultimately improving the environmental impact of the plant.

42 ENGINEERING↗
DOE OSTI2022

High-throughput investigation of structural evolution upon solid-state in Cu–Cr–Co combinatorial multilayer thin-film

Cu-Cr-Co combinatorial multilayer thin-films were prepared by a high-throughput ion beam sputtering system. Based on the thickness ratio among the individual nanoscale monolayers (Cu, Cr, Co), the resulting stoichiometry covered the entire phase diagram. The chemical composition and structure of Cu-Cr-Co combinatorial chip upon solid-state reaction were studied by lab-based micro-X-ray fluorescence (mu-XRF) and high-throughput synchrotron X-ray diffraction (XRD), respectively. A composition-structure map for Cu-Cr-Co combinatorial chip was developed through automated data analysis employing hierarchical clustering techniques. The structural evolution of Cu-Cr-Co combinatorial chip as a function of heat-treatment temperature, time, and modulation period were studied systematically. Furthermore, the effect of the elemental distribution in-depth direction was investigated to gain more insights regarding phase transformation. This work provides an efficient method and new perspectives for the design and optimization of the composition and structure of high-performance thin-films.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Energetics and Structure of Ag–Water Clusters Formed in Mordenite

Zeolite-supported Ag 0 clusters have broad applications from catalysis to medicine, necessitating a mechanistic understanding of the formation of Ag 0 clusters in situ. Density functional theory (DFT) simulations have been performed on silver, water, and silver–water clusters in silica mordenite (Si-MOR), to identify the role of the confinement on the structure and energetics of Ag 0 cluster formation. The most favorable binding energy in the 12-membered ring (MR) pore of the Si-MOR is a 10–15-atom Ag 0 cluster. Computational pair distribution function (PDF) data indicates that the Ag 0 and Ag 0 –H 2 O clusters formed in vacuum versus in Si-MOR exhibit structural differences. Additionally, when the Ag 0 cluster is confined, the density decreases and the surface area increases, hypothesized to be due to the limiting geometry of the 12-MR main channel. An energetic drive toward formation of larger Ag 0 clusters was also identified, with hydrated silver atoms generating higher energy structures. In conclusion, this work identifies mechanistic and structural insight into the role of nanoconfinement on formation of Ag 0 clusters in mordenite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Urban Forests as Main Regulator of the Evaporative Cooling Effect in Cities

Abstract Higher temperatures in urban areas expose a large fraction of the human population to potentially dangerous heat stress. Green spaces are promoted worldwide as local and city‐scale cooling strategies but the amount, type, and functioning of vegetation in cities lack quantification and their interaction with urban climate in different settings remains a matter of debate. Here we use state‐of‐the‐art remote sensing data from 145 city clusters to disentangle the drivers of surface urban heat islands (SUHI) intensity and quantify urban‐rural differences in vegetation cover, species composition, and evaporative cooling. We show that nighttime SUHIs are affected mostly by abiotic factors, while daytime SUHIs are highly correlated with vegetation characteristics and the wetness of the background climate. Magnitude and seasonality of daytime SUHIs are controlled by urban‐rural differences in plant transpiration and leaf area, which explain the dependence of SUHIs on wetness conditions. Leaf area differences are caused primarily by changes in vegetation type and a loss of in‐city forested areas, highlighting the importance of maintaining “natural reserves” as a sustainable heat mitigation policy.

Paschalis, Athanasios↗
DOE OSTI2022

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Substructure in the stellar halo near the Sun: II. Characterisation of independent structures

In an accompanying paper, we present a data-driven method for clustering in ‘integrals of motion’ space and apply it to a large sample of nearby halo stars with 6D phase-space information. The algorithm identified a large number of clusters, many of which could tentatively be merged into larger groups. The goal here is to establish the reality of the clusters and groups through a combined study of their stellar populations (average age, metallicity, and chemical and dynamical properties) to gain more insights into the accretion history of the Milky Way. To this end, we developed a procedure that quantifies the similarity of clusters based on the Kolmogorov–Smirnov test using their metallicity distribution functions, and an isochrone fitting method to determine their average age, which is also used to compare the distribution of stars in the colour–absolute magnitude diagram. Also taking into consideration how the clusters are distributed in integrals of motion space allows us to group clusters into substructures and to compare substructures with one another. We find that the 67 clusters identified by our algorithm can be merged into 12 extended substructures and 8 small clusters that remain as such. The large substructures include the previously known Gaia-Enceladus, Helmi streams, Sequoia, and Thamnos 1 and 2. We identify a few over-densities that can be associated with the hot thick disc and host a small metal-poor population. Especially notable is the largest (by number of member stars) substructure in our sample which, although peaking at the metallicity characteristic of the thick disc, has a very well populated metal-poor component, and dynamics intermediate between the hot thick disc and the halo. We also identify additional debris in the region occupied by Sequoia with clearly distinct kinematics, likely remnants of three different accretion events with progenitors of similar masses. Although only a small subset of the stars in our sample have chemical abundance information, we are able to identify different trends of [Mg/Fe] versus [Fe/H] for the various substructures, confirming our dissection of the nearby halo. We find that at least 20% of the halo near the Sun is associated to substructures. When comparing their global properties, we note that those substructures on retrograde orbits are not only more metal-poor on average but are also older. We provide a table summarising the properties of the substructures, as well as a membership list that can be used for follow-up chemical abundance studies for example.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Atomic orbital-based SOS-MP2 with tensor hypercontraction. I. GPU-based tensor construction and exploiting sparsity

We present a tensor hypercontracted (THC) scaled opposite spin second order Møller-Plesset perturbation theory (SOS-MP2) method. By using THC, we reduce the formal scaling of SOS-MP2 with respect to molecular size from quartic to cubic. We achieve further efficiency by exploiting sparsity in the atomic orbitals and using graphical processing units (GPUs) to accelerate integral construction and matrix multiplication. The practical scaling of GPU-accelerated atomic orbital-based THC-SOS-MP2 calculations is found to be N{sup 2.6} for reference data sets of water clusters and alanine polypeptides containing up to 1600 basis functions. The errors in correlation energy with respect to density-fitting-SOS-MP2 are less than 0.5 kcal/mol for all systems tested (up to 162 atoms).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Observation of propane cluster size distributions during nucleation and growth in a Laval expansion

We report on molecular-level studies of the condensation of propane gas and propane/ethane gas mixtures in the uniform (constant pressure and temperature) postnozzle flow of Laval expansions using soft single-photon ionization by vacuum ultraviolet light and mass spectrometric detection. The whole process, from the nucleation to the growth to molecular aggregates of sizes of several nanometers (∼5 nm), can be monitored at the molecular level with high time-resolution (∼3 μs) for a broad range of pressures and temperatures. For each time, pressure, and temperature, a whole mass spectrum is recorded, which allows one to determine the critical cluster size range for nucleation as well as the kinetics and mechanisms of cluster-size specific growth. The detailed information about the size, composition, and population of individual molecular clusters upon condensation provides unique experimental data for comparison with future molecular-level simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The many-body electronic interactions of Fe(II)–porphyrin

Fe(II)–porphyrin complexes exhibit a diverse range of electronic interactions between the metal and macrocycle. Herein, the incremental full configuration interaction method is applied to the entire space of valence orbitals of a Fe(II)–porphyrin model using a modest basis set. A novel visualization framework is proposed to analyze individual many-body contributions to the correlation energy, providing detailed maps of this complex’s highly correlated electronic structure. Furthermore, this technique is used to parse the numerous interactions of two low-lying triplet states ( 3 A 2 g and 3 E g ) and to show that strong metal d–d and macrocycle π–π orbital interactions preferentially stabilize the 3 A 2 g state. d–π interactions, on the other hand, preferentially stabilize the 3 E g state and primarily appear when correlating six electrons at a time. Ultimately, the Fe(II)–porphyrin model’s full set of 88 valence electrons are correlated in 275 orbitals, showing the interactions up to the 4-body level, which covers the great majority of correlations in this system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Regulators of early maize leaf development inferred from transcriptomes of laser capture microdissection (LCM)-isolated embryonic leaf cells

The superior photosynthetic efficiency of C 4 leaves over C 3 leaves is owing to their unique Kranz anatomy, in which the vein is surrounded by one layer of bundle sheath (BS) cells and one layer of mesophyll (M) cells. Kranz anatomy development starts from three contiguous ground meristem (GM) cells, but its regulators and underlying molecular mechanism are largely unknown. To identify the regulators, we obtained the transcriptomes of 11 maize embryonic leaf cell types from five stages of pre-Kranz cells starting from median GM cells and six stages of pre-M cells starting from undifferentiated cells. Principal component and clustering analyses of transcriptomic data revealed rapid pre-Kranz cell differentiation in the first two stages but slow differentiation in the last three stages, suggesting early Kranz cell fate determination. In contrast, pre-M cells exhibit a more prolonged transcriptional differentiation process. Differential gene expression and coexpression analyses identified gene coexpression modules, one of which included 3 auxin transporter and 18 transcription factor (TF) genes, including known regulators of Kranz anatomy and/or vascular development. In situ hybridization of 11 TF genes validated their expression in early Kranz development. We determined the binding motifs of 15 TFs, predicted TF target gene relationships among the 18 TF and 3 auxin transporter genes, and validated 67 predictions by electrophoresis mobility shift assay. From these data, we constructed a gene regulatory network for Kranz development. Our study sheds light on the regulation of early maize leaf development and provides candidate leaf development regulators for future study.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

THE ROLE OF THERMOHALINE MIXING IN INTERMEDIATE- AND LOW-METALLICITY GLOBULAR CLUSTERS

It is now widely accepted that globular cluster red giant branch (RGB) stars owe their strange abundance patterns to a combination of pollution from progenitor stars and in situ extra mixing. In this hybrid theory a first generation of stars imprints abundance patterns into the gas from which a second generation forms. The hybrid theory suggests that extra mixing is operating in both populations and we use the variation of [C/Fe] with luminosity to examine how efficient this mixing is. We investigate the observed RGBs of M3, M13, M92, M15, and NGC 5466 as a means to test a theory of thermohaline mixing. The second parameter pair M3 and M13 are of intermediate metallicity and our models are able to account for the evolution of carbon along the RGB in both clusters, although in order to fit the most carbon-depleted main-sequence stars in M13 we require a model whose initial [C/Fe] abundance leads to a carbon abundance lower than is observed. Furthermore, our results suggest that stars in M13 formed with some primary nitrogen (higher C+N+O than stars in M3). In the metal-poor regime only NGC 5466 can be tentatively explained by thermohaline mixing operating in multiple populations. We find thermohaline mixing unable to model the depletion of [C/Fe] with magnitude in M92 and M15. It appears as if extra mixing is occurring before the luminosity function bump in these clusters. To reconcile the data with the models would require first dredge-up to be deeper than found in extant models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

THE INFLUENCE OF ENVIRONMENT ON THE CIRCUMGALACTIC MEDIUM

The effect of environment on the circumgalactic medium (CGM) is investigated through a comparison of Ly{alpha} absorption line data in the Virgo Cluster and the field. This Letter uses the first systematic survey of background QSOs in and around the Virgo Cluster and large existing surveys of galaxies at low redshift. While previous studies found denser gas (higher equivalent width) closer to a galaxy (lower impact parameter), this correlation disappears in the Virgo environment. In addition, the covering fraction of the CGM is lower in the cluster environment than in the circumcluster environment and field. The results indicate that the CGM is suppressed for cluster galaxies while galaxies in the circumcluster environment have abundant CGM. The truncation of the CGM may result in the quenching of star formation through starvation. Our results also show that CGM surveys must consider the role of environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

PySFD: comprehensive molecular insights from significant feature differences detected among many simulated ensembles

Motivation Many modeling analyses of molecular dynamics (MD) simulations are based on a definition of states that can be (groups of) clusters of simulation frames in a feature space composed of molecular coordinates. With increasing dimension of this feature space (due to the increasing size or complexity of a simulated molecule), it becomes very difficult to cluster the underlying MD data and estimate a statistically robust model. To mitigate this “curse of dimensionality”, one can reduce the feature space, e.g., with principal component or time-lagged independent component analysis transformations, focusing the analysis on the most important modes of transitions. In practice, however, all these reduction strategies may neglect important molecular details that are susceptible to experimental verification. Results To recover such molecular details, I have developed PySFD (Significant Feature Differences analyzer for Python), a multi-processing software package that efficiently selects significantly different features of any user-defined feature type among potentially many different simulated state ensembles, such as meta-stable states of a Markov State Model (MSM). Applying PySFD on MSMs of an aggregate of 300 microseconds MD simulations recently performed on the major histocompatibility complex class II (MHCII) protein, I demonstrate how this toolkit can extract and visualize valuable mechanistic information from big MD simulation data, e.g., in form of networks of dynamic interaction changes connecting functionally relevant sites of a protein complex.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Improving galaxy clustering measurements with deep learning: analysis of the DECaLS DR7 data

ABSTRACT Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper, we present, validate, and implement the idea of adopting the systematics mitigation method of artificial neural networks for modelling the relationship between the target galaxy density field and various observational realities including but not limited to Galactic extinction, seeing, and stellar density. Our method by construction allows a wide class of models and alleviates overtraining by performing k-fold cross-validation and dimensionality reduction via backward feature elimination. By permuting the choice of the training, validation, and test sets, we construct a selection mask for the entire footprint. We apply our method on the extended Baryon Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs) selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and show that the spurious large-scale contamination due to imaging systematics can be significantly reduced by up-weighting the observed galaxy density using the selection mask from the neural network and that our method is more effective than the conventional linear and quadratic polynomial functions. We perform extensive analyses on simulated mock data sets with and without systematic effects. Our analyses indicate that our methodology is more robust to overfitting compared to the conventional methods. This method can be utilized in the catalogue generation of future spectroscopic galaxy surveys such as eBOSS and Dark Energy Spectroscopic Instrument (DESI) to better mitigate observational systematics.

79 ASTRONOMY AND ASTROPHYSICS↗