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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Disintegration of Long-period Comet C/2019 Y4 (ATLAS). I. Hubble Space Telescope Observations

The near-Sun comet C/2019 Y4 (ATLAS) is the first member of a long-period comet group observed to disintegrate well before perihelion. Here we present our investigation into this disintegration event using images obtained in a three-day Hubble Space Telescope campaign. We identify two fragment clusters produced by the initial disintegration event, corresponding to fragments C/2019 Y4-A and C/2019 Y4-B identified in ground-based data. These two clusters started with similar integrated brightness but exhibit different evolutionary behavior. C/2019 Y4-A was much shorter-lived compared to C/2019 Y4-B and showed signs of significant mass loss and changes in size distribution throughout the three-day campaign. The cause of the initial fragmentation is undetermined by the limited evidence but crudely compatible with either the spin-up disruption of the nucleus or runaway sublimation of subsurface supervolatile ices, either of which would lead to the release of a large amount of gas as inferred from the significant bluing of the comet observed shortly before its disintegration. Gas can only be produced by the sublimation of volatile ices, which must have survived at least one perihelion passage at a perihelion distance of q = 0.25 au. We speculate that Comet ATLAS is derived from the ice-rich interior of a nonuniform, kilometer-wide progenitor that split during its previous perihelion. This suggests that comets down to a few kilometers in diameter can still possess complex, nonuniform interiors that can protect ices against intense solar heating.

47 OTHER INSTRUMENTATION↗
NASA NTRS1974

Proper motions, cluster membership and reddening in NGC 6611

Approaches used in reductions for proper motion are based on methods developed by Vasilevskis et al. (1965). Probabilities of cluster membership were determined by fitting the distribution of stars in the proper motion vector diagram with two bivariate Gaussian distributions for the field and the cluster stars. The spectroscopic data indicate that the stellar content of the cluster is not anomalous. The color-magnitude diagram is considered along with questions regarding member stars lying above the main sequence, the cluster age, and aspects of spatial structure.

Kamp, L. W.↗
NASA NTRS1984

Convective rain rates and their evolution during storms in a semiarid climate

The semiarid climate of the U.S. northern High Plains region has been studied with respect to rain rates and their evolution during summertime convective storms, using radar data from a total of 750 radar echo clusters. Analysis of this data suggests that the average rain rate R among storms is in a first approximation independent of the total rain volume, if the entire storm duration is considered in the averaging process. R primarily depends on the reflectivity threshold considered in calculating the area coverage integrated over the lifetime of the storm. R evolution during storms is analyzed by dividing each storm lifetime into 10 min, 1, 2, and 4 hours, as well as growing and decaying periods. The value of R remained independent of the total rain volume when the growing or decaying periods of storms were considered separately.

Doneaud, A. A.↗
NASA NTRS2004

Research on Spectroscopy, Opacity, and Atmospheres

I propose to continue providing observers with basic data for interpreting spectra from stars, novas, supernovas, clusters, and galaxies. These data will include allowed forbidden line lists both laboratory and computed, for the first five to ten ions of all atoms and for all relevant diatomic molecules. I will eventually expend to all ions of the first thirty elements to treat far UV end X-ray spectra, and for envelope opacities. I also include triatomic molecules providing by other researchers. I have made CDs with Partridge and Schwanke's water data for work on M stars.The luna data also serve as input to my model atmosphere and synthesis programs that generated energy distributions, photometry, limb darkening, and spectra that can be used for planning observations and for fitting observed spectra. The spectrum synthesis programs produce detailed plots with the line identified. Grids of stellar spectra can be used for radial velocity-, rotation-, or abundance templates and for population synthesis. I am fitting spectra of bright stars to test the data and to produce atlases to guide observer. For each star the whole spectrum is computed from the UV to the far IR. The line data, opacities, models, spectra, and programs are freely distributed on CDs and on my web site and represent a unique resource for many NASA programs.

Oliversen, Ronald↗
NASA NTRS2005

Research on Spectroscopy, Opacity, and Atmospheres

I propose to continue providing observers with basic data for interpreting spectra from stars, novas, supernovas, clusters, and galaxies. These data will include allowed and forbidden line lists, both laboratory and computed, for the first five to ten ions of all atoms and for all relevant diatomic molecules. I will eventually expand to all ions of the first thirty elements to treat far UV and X-ray spectra, and for envelope opacities. I also include triatomic molecules provided by other researchers. I have also made CDs with Partridge and Schwenke's water data for work on UV stars. The line data also serve as input to my model atmosphere and synthesis programs that generate energy distributions, photometry, limb darkening, and spectra that can be used for planning observations and for fitting observed spectra. The spectrum synthesis programs produce detailed plots with the lines identified. Grids of stellar spectra can be used for radial velocity-, rotation-, or abundance templates and for population synthesis. I am fitting spectra of bright stars to test the data and to produce atlases to guide observers. For each star the whole spectrum is computed from the UV to the far IR. The line data, opacities, models, spectra, and programs are freely distributed on CDs and on my Web site and represent a unique resource for many NASA programs. I am now in full production of new line lists for atoms. I am computing all ions of all elements from H to Zn and the first 5 ions of all the heavier elements, about 800 ions. For each ion I treat as many as 61 even and 61 odd configurations, computing all energy levels and eigenvectors. The Hamiltonian is determined from a scaled-Hartree-Fock starting guess by least squares fitting the observed energy levels. The average energy of each configuration is used in computing scaled-Thomas-Fermi-Dirac wavefunctions for each configuration which in turn are used to compute allowed and forbidden transition integrals. These are multiplied into the LS allowed and forbidden transition arrays. The transition arrays are transformed to the observed coupling to yield the allowed and forbidden line lists. Results are put on the web as they are finished. Provided I get funding,there will be more than 500 million lines. I will then compare ion by ion, to all the laboratory and computed data in the literature and make up a working line list for spectrum synthesis and opacity calculations with the best available data. As the laboratory spectrum analyses are improved, I will redo the calculations with the new energy levels. My original plan when I started the new calculations was to run through all the atoms using my old Cray programs from the 1980's that were limited to 1100 x 1100 arrays in the Hamiltonian for each J. Then I would go back and rerun the more complicated cases with 3000 x 3000 arrays so that I could include many more configurations and more configuration interactions. At present I am limited to 61 even and 61 odd configurations and I try to include everything up through n = 9. The current program runs on Alpha workstations. I decided to test the big program on Fe I and Fe II to see whether there was any great difference in the low configurations compared to those from the Cray program. Besides increasing the number of E1 lines by a factor of 6 to 7.7 million, there was an unexpected result: the electric quadrupole transitions were 10 times stronger than before because the transition integrals are weighted by r(exp 2) ---they become very large for high n, and because there are numerous configuration interactions that mix the low and high configurations. As a check I was able to reproduce Carstang's (1962) lower results by running his three configurations with my program. Since my model atom is still only a subset of a real Fe II ion, the true quadrupole A values are probably larger than mine. The magnetic dipole lines are affected by the mixing but the overall scale does not change. Because of this scovery I decided that there was no point in computing the small array cases. I have been running with as many configurations as I can and with thousands of parameters in the Hamiltonian. The computer runs take much longer to set up and produce than I had expected. I have concentrated on redoing the low iron group spectra, especially to get data for supernova modelers. I have done only Ca I -- Zn I, Ca II -- Zn II, CU I -- Cu XXIX, Zn I - Zn XXX, for practice at high stages of ionization, C I, C II, S I, and CL I and Ag I for people who were working on the laboratory spectra. Check my web site kurucz.harvard.edu for current additions. My latest calculations have been for carbon I and sulphur I, and silicon I is under way using the same elaborate approach as for C I, which took many months to do. These line lists greatly increase the number of lines in the ultraviolet, in the visible, and especially in the infrared. They will increase the opacity in A, F, and G stars. They will account for many unidentified lines in the sun.

Kurucz, Robert L.↗
NASA NTRS2012

NASA Center for Climate Simulation (NCCS) Presentation

The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.

Webster, William P.↗
DOE OSTI2026

Frequentist cosmological constraints from full-shape clustering measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.

Bayesian reasoning↗
DOE OSTI2015

On the merging cluster Abell 578 and its central radio galaxy 4C+67.13

Here we analyze radio, optical, and X-ray data for the peculiar cluster Abell 578. This cluster is not fully relaxed and consists of two merging sub-systems. The brightest cluster galaxy (BCG), CGPG 0719.8+6704, is a pair of interacting ellipticals with projected separation ∼10 kpc, the brighter of which hosts the radio source 4C+67.13. The Fanaroff–Riley type-II radio morphology of 4C+67.13 is unusual for central radio galaxies in local Abell clusters. Our new optical spectroscopy revealed that both nuclei of the CGPG 0719.8+6704 pair are active, albeit at low accretion rates corresponding to the Eddington ratio ∼10{sup −4} (for the estimated black hole masses of ∼3×10{sup 8} M{sub ⊙} and ∼10{sup 9} M{sub ⊙}). The gathered X-ray (Chandra) data allowed us to confirm and to quantify robustly the previously noted elongation of the gaseous atmosphere in the dominant sub-cluster, as well as a large spatial offset (∼60 kpc projected) between the position of the BCG and the cluster center inferred from the modeling of the X-ray surface brightness distribution. Detailed analysis of the brightness profiles and temperature revealed also that the cluster gas in the vicinity of 4C+67.13 is compressed (by a factor of about ∼1.4) and heated (from ≃2.0 keV up to 2.7 keV), consistent with the presence of a weak shock (Mach number ∼1.3) driven by the expanding jet cocoon. This would then require the jet kinetic power of the order of ∼10{sup 45} erg s{sup −1}, implying either a very high efficiency of the jet production for the current accretion rate, or a highly modulated jet/accretion activity in the system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers

High–dimensional datasets are becoming ubiquitous in many applications and therefore unsupervised tensor methods to interrogate them are needed. Here, we report a new unsupervised machine learning (ML) approach (NTFk) based on nonnegative tensor factorization integrated with a custom k–means clustering. We demonstrate the ability of NTFk to extracting temporal and spatial features of phase separation of copolymers as they are modeled by self–consistent field theory. Microphase separation of block copolymers has been extensively studied both experimentally and theoretically. However, the interpretation of computer simulations and/or experimental data, representing temporal and spatial changes of molecular species concentration is still a challenging task. Thus, extracting the phase diagram from simulations or experimental data as well as the interpretation of data requires discernment of the model/experimental parameters (such as, temperature, concentrations, the number of molecular species and the interaction between species) impact on the microphase separation process. An attractive and unique aspect of the introduced ML method is that it ensures the nonnegativity of the extracted latent features. Nonnegativity is an essential constraint needed to obtain interpretable and sparse latent features that are parts–based representation of the data. The custom clustering in NTFk serves to estimate the number of latent features in the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Exploring Memory Hierarchy to Improve Scientific Data Read Performance, In: 2015 IEEE International Conference on Cluster Computing

Improving read performance is one of the major challenges with speeding up scientific data analytic applications. Utilizing the memory hierarchy is one major line of researches to address the read performance bottleneck. Related methods usually combine solide-state-drives(SSDs) with dynamic random-access memory(DRAM) and/or parallel file system(PFS) to mitigate the speed and space gap between DRAM and PFS. However, these methods are unable to handle key performance issues plaguing SSDs, namely read contention that may cause up to 50% performance reduction. In this paper, we propose a framework that exploits the memory hierarchy resource to address the read contention issues involved with SSDs. The framework employs a general purpose online read algorithm that able to detect and utilize memory hierarchy resource to relieve the problem. To maintain a near optimal operating environment for SSDs, the framework is able to orchastrate data chunks across different memory layers to facilitate the read algorithm. Compared to existing tools, our framework achieves up to 50% read performance improvement when tested on datasets from real-world scientific simulations.

Computer Science↗
DOE OSTI2023

Anomaly detection for MPC forecast in Fleet of Water Heaters

Among residential devices, water heaters consume 20% of home energy use in the United States. Water heaters possess the capability to store energy within their reservoirs, enabling the ability to decouple energy use from hot water use. This capability can be used to reduce energy usage and costs while also supporting grid services. This requires accurate forecasting of the parameters of the water heater such as upper and lower temperatures. In this study, we analyzed the performance and behavior of a water heater model used in the real-world to predict a control mechanism that is implemented in a smart residential neighborhood. The model forecasts are accurate in most cases but not all. In such scenarios, error correction of the model is necessary to further improve model predictive control accuracy. Anomaly detection is the first step of error correction. This study complements existing research by grouping time series data into two clusters one with anomalies and another without anomalies. To achieve this task, we explored and compared multiple unsupervised machine learning algorithms to perform clustering. Among these algorithms, Ward clustering has the lowest running time and identified the highest number of anomalies for the upper temperature limit. The proposed approach is tested based on the data collected in a neighborhood with 46 townhomes located in Atlanta, GA.

Lebakula, Viswadeep↗
NASA NTRS2000

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

DNA/radiation effects↗
NASA NTRS1985

Applications of Fuzzy Set Theory to Satellite Soundings

The introduction of an appropriate fuzzy setting for satellite soundings and its application to clustering methods via unimodal fuzzy sets in the future is proposed. Methods of hard clustering analysis and fuzzy partitioned clustering were applied on simulated data with very encouraging results. The proposed clustering technique is discussed. The notion of a unimodal fuzzy set was chosen to represent the partition of a data set for two reasons: (1) it detects all the locations in the vector space where highly concentrated clusters of points exist; and (2) the notion is general enough to represent clusters that exhibit quite general distributions of points. The technique detects all of the existing unimodal fuzzy sets and realizes the maximum separation among them. It is economical in memory space and computational time requirements and also detects groups that are fairly generally distributed in the feature space.

Munteanu, M. J.↗
NASA NTRS1985

A cluster analysis method for identification of subpopulations of cells in flow cytometric list-mode arrays

A specialized program was developed for flow cytometric list-mode data using an heirarchical tree method for identifying and enumerating individual subpopulations, the method of principal components for a two-dimensional display of 6-parameter data array, and a standard sorting algorithm for characterizing subpopulations. The program was tested against a published data set subjected to cluster analysis and experimental data sets from controlled flow cytometry experiments using a Coulter Electronics EPICS V Cell Sorter. A version of the program in compiled BASIC is usable on a 16-bit microcomputer with the MS-DOS operating system. It is specialized for 6 parameters and up to 20,000 cells. Its two-dimensional display of Euclidean distances reveals clusters clearly, as does its 1-dimensional display. The identified subpopulations can, in suitable experiments, be related to functional subpopulations of cells.

Li, Z. K.↗
DOE OSTI2022

Elucidating the Roles of Local and Nonlocal Rate Enhancement Mechanisms in Plasmonic Catalysis

Plasmonic metal nanoparticles (e.g., Ag, Au, and Cu) constitute a class of materials that interact with light via the excitation of localized surface plasmon resonance (LSPR). Numerous studies have reported substantial enhancements in the rates of chemical reactions on illuminated plasmonic nanoparticle catalysts compared to corresponding systems in the absence of illumination. There are two mechanisms that have been proposed to explain the LSPR-induced chemical reactivity. One mechanism assumes a local plasmon-induced hot charge-carrier-mediated activation of the reactants, while the other assumes an LSPR-induced equilibrium heating of the catalyst, which leads to energy transfer to and chemical reaction of the adsorbed reactants. Here, in this contribution, we developed a setup amenable to accurate in situ catalyst temperature and kinetic reaction rate measurements. We employed this setup to study the LSPR-induced rate enhancement in a case study of the CO oxidation reaction on plasmonic, monometallic Ag nanoparticle catalysts supported on α-Al 2 O 3 . We explored various Ag loadings and clustering levels. Our data show that the equilibrium heating of the catalyst cannot fully explain the illumination-induced plasmonic rate enhancements. This is the case even for high loading and clustering of Ag nanoparticles, where the equilibrium heating significantly increases. Based on the analysis, we propose that local effects, related to the plasmon-induced activation of adsorbates (reactants) via electronic excitation of the reactant or photothermal heating of the reactants that is highly localized to the individual nanoparticles, play a critical role in driving LSPR-induced chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

THE IMACS CLUSTER BUILDING SURVEY. III. THE STAR FORMATION HISTORIES OF FIELD GALAXIES

Using data from the IMACS Cluster Building Survey and from nearby galaxy surveys, we examine the evolution of the rate of star formation in field galaxies from z = 0.60 to the present. Fitting the luminosity function to a standard Schechter form, we find a rapid evolution of M{sub B}{sup *} consistent with that found in other deep surveys; at the present epoch M{sub B}{sup *} is evolving at the rate of 0.38 Gyr{sup -1}, several times faster than the predictions of simple models for the evolution of old, coeval galaxies. The evolution of the distribution of specific star formation rates (SSFRs) is also too rapid to explain by such models. We demonstrate that starbursts cannot, even in principle, explain the evolution of the SSFR distribution. However, the rapid evolution of both M{sub B}{sup *} and the SSFR distribution can be explained if some fraction of galaxies have star formation rates characterized by both short rise and fall times and by an epoch of peak star formation more recent than the majority of galaxies. Although galaxies of every stellar mass up to 1.4 Multiplication-Sign 10{sup 11} M{sub Sun} show a range of epochs of peak star formation, the fraction of ''younger'' galaxies falls from about 40% at a mass of 4 Multiplication-Sign 10{sup 10} M{sub Sun} to zero at a mass of 1.4 Multiplication-Sign 10{sup 11} M{sub Sun }. The incidence of younger galaxies appears to be insensitive to the density of the local environment; but does depend on group membership: relatively isolated galaxies are much more likely to be young than are group members.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Scalable deep text comprehension for Cancer surveillance on high-performance computing

Background: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.Results: We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs.Conclusions: We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Qiu, John X.↗