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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Opinion dynamics in financial markets via random networks

We investigate financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize individuals in a financial market according to their trading strategy, namely, whether they are noise traders or fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the decisions of fundamentalist agents. We introduce a noise parameter, q , to represent the level of anxiety and perceived uncertainty regarding market behavior, enabling the possibility of adrift financial action. We place individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interactions. At any given time, individuals assume one of two possible opinion states ±1 regarding buying or selling an asset. The model exhibits fundamental qualitative and quantitative real-world market features such as the distribution of logarithmic returns with fat tails, clustered volatility, and the long-term correlation of returns. We use Student’s t distributions to fit the histograms of logarithmic returns, showing a gradual shift from a leptokurtic to a mesokurtic regime depending on the fraction of fundamentalist agents. Furthermore, we compare our results with those concerning the distribution of the logarithmic returns of several real-world financial indices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Volumetric imaging of the 3D orientation of cellular structures with a polarized fluorescence light-sheet microscope

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations in biological samples, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the diffraction-limited three-dimensional distribution of the orientations and positions of ensembles of fluorescent dipoles that label biological structures. We share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model the distributions based on the polarization-dependent efficiency of excitation and detection of emitted fluorescence, using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labeled giant unilamellar vesicles, fast-scarlet-labeled cellulose in xylem cells, and phalloidin-labeled actin in U2OS cells. Additionally, we observe phalloidin-labeled actin in mouse fibroblasts grown on grids of labeled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Science & Technology - Other Topics↗
DOE OSTI2025

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors (MRs) pose new challenges for international safeguards. Here, their small size and mass reproducibility make them ideal for deployment in greater numbers and in remote locations, making the job of safeguards inspectors more challenging. Machine learning (ML) is currently being applied to many fields to augment human performance and increase automation; in particular, ML could be used to provide insight for international inspectors to help detect the diversion of nuclear fuel from MR cores. Four ML model types (k-nearest neighbors, decision tree, random forest, and histogram-based gradient boosted ensemble) were trained on integrated flux and critical control drum angle data generated with Serpent 2 for a realistic heat pipe MR design, achieving nearly 100% binary classification accuracy of nominal and diversion core configurations by the end of 1 full power year for three of the four model types. Regression model variants were also trained, using the same input data, for predicting the number of fuel pins diverted. Root-mean-square errors below 5% of the total number of fuel pins were achieved by the 1 full power year mark for all models.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Minimum local distance density estimation

We present a local density estimator based on first-order statistics. To estimate the density at a point, $x$, the original sample is divided into subsets and the average minimum sample distance to $x$ over all such subsets is used to define the density estimate at $x$. The tuning parameter is thus the number of subsets instead of the typical bandwidth of kernel or histogram-based density estimators. The proposed method is similar to nearest-neighbor density estimators but it provides smoother estimates. We derive the asymptotic distribution of this minimum sample distance statistic to study globally optimal values for the number and size of the subsets. Simulations are used to illustrate and compare the convergence properties of the estimator. The results show that the method provides good estimates of a wide variety of densities without changes of the tuning parameter, and that it offers competitive convergence performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Wang–Landau approach to the simulation of water clusters

The Wang–Landau Monte Carlo method [Wang FG, Landau DP. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys Rev Lett. 2001;86:2050-2053; Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. Phys Rev E. 2001;64:056101] has been established as an effective computational approach for studying complex systems. In this study, we review the properties of 6 popular empirical water potentials, including a recently proposed explicit 3-body potential, obtained by applying the Wang–Landau method to the simulation of water clusters. Finally, comparing to numerous classical and first principle calculations, we show that several classical water models can provide a consistent description of the structural and thermodynamical properties of water clusters partially in agreement of ab initio results, although they were designed to reproduce macroscopic properties of water at ambient conditions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2014

Properties of optically selected supernova remnant candidates in M33

Narrowband images covering strong emission lines are efficient for surveying supernova remnants (SNRs) in nearby galaxies. Using the narrowband images provided by the Local Group Galaxy Survey, we searched for SNRs in M33. Culling the objects with enhanced [S II]/Hα and round morphology in the continuum-subtracted Hα and [S II] images, we produced a list of 199 sources. Among them, 79 are previously unknown. Their progenitor and morphology types were classified. A majority of the sample (170 objects) are likely remnants of core-collapse supernovae (SNe), and 29 are remnants of Type Ia SNe. The cumulative size distribution of these objects is found to be similar to that of the M31 remnants derived in a similar way. We obtain a power-law slope, α = 2.38 ± 0.05. Thus, a majority of the sources are considered to be in the Sedov-Taylor phase, consistent with previous findings. The histogram of the emission-line ratio ([S II]/Hα) of the remnants has two concentrations at [S II]/Hα ∼ 0.55 and ∼0.8, as in M31. Interestingly, L {sub X} (and L {sub 20cm}) of the compact center-bright objects are correlated with their optical luminosity. The remnants with X-ray emission have brighter optical surface brightnesses and smaller diameters than those without X-ray emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The spatial structure of young stellar clusters. II. Total young stellar populations

We investigate the intrinsic stellar populations (estimated total numbers of OB and pre-main-sequence stars down to 0.1 M{sub ⊙}) that are present in 17 massive star-forming regions (MSFRs) surveyed by the MYStIX project. The study is based on the catalog of >31,000 MYStIX Probable Complex Members with both disk-bearing and disk-free populations, compensating for extinction, nebulosity, and crowding effects. Correction for observational sensitivities is made using the X-ray luminosity function and the near-infrared initial mass function—a correction that is often not made by infrared surveys of young stars. The resulting maps of the projected structure of the young stellar populations, in units of intrinsic stellar surface density, allow direct comparison between different regions. Several regions have multiple dense clumps, similar in size and density to the Orion Nebula Cluster. The highest projected density of ∼34,000 stars pc{sup −2} is found in the core of the RCW 38 cluster. Histograms of surface density show different ranges of values in different regions, supporting the conclusion of Bressert et al. that no universal surface-density threshold can distinguish between clustered and distributed star formation. However, a large component of the young stellar population of MSFRs resides in dense environments of 200–10,000 stars pc{sup −2} (including within the nearby Orion molecular clouds), and we find that there is no evidence for the B10 conclusion that such dense regions form an extreme “tail” of the distribution. Tables of intrinsic populations for these regions are used in our companion study of young cluster properties and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The statistics of BAT-to-XRT flux ratio in GRBs: Evidence for a characteristic value and its implications

We present the statistics of the ratio, R, between the prompt and afterglow “plateau” fluxes of gamma-ray bursts (GRBs). We define this as the ratio of the mean prompt energy flux in Swift BAT and the Swift XRT one, immediately following the steep transition between these two states and the beginning of the afterglow stage referred to as the “plateau”. Like the distribution of many other GRB observables, the histogram of R is log-normal with maximum at a value R{sub m}≃2000, FWHM of about two decades, and with the entire distribution spanning about five decades in the value of R. We note that the peak of the distribution is close to the proton-to-electron mass ratio (R{sub m}≃m{sub p}/m{sub e}=1836), as proposed to be the case in an earlier publication, on the basis of a specific model of the GRB dissipation process. It therefore appears that, in addition to the values of the energy of peak luminosity E{sub pk}∼m{sub e}c{sup 2}, GRBs present us with one more quantity with an apparent characteristic value. The fact that the values of both these quantities (E{sub pk} and R) are consistent with the same specific model invoked to account for the efficient conversion of their relativistic proton energies to electrons argues favorably for its underlying assumptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

An atom probe perspective on phase separation and precipitation in duplex stainless steels

Here, three-dimensional chemical imaging of Fe–Cr alloys showing Fe-rich (α)/Cr-rich (α') phase separation is reported using atom probe tomography techniques. The extent of phase separation, i.e., amplitude and wavelength, has been quantitatively assessed using the Langer-Bar-on-Miller, proximity histogram, and autocorrelation function methods for two separate Fe–Cr alloys, designated 2101 and 2205. Although the 2101 alloy possesses a larger wavelength and amplitude after annealing at 427 °C for 100–10 000 h, it exhibits a lower hardness than the 2205 alloy. In addition to this phase separation, ultra-fine Ni–Mn–Si–Cu-rich G-phase precipitates form at the α/α' interfaces in both alloys. For the 2101 alloy, Cu clusters act to form a nucleus, around which a Ni–Mn–Si shell develops during the precipitation process. For the 2205 alloy, the Ni and Cu atoms enrich simultaneously and no core–shell chemical distribution was found. This segregation phenomenon may arise from the exact Ni/Cu ratio inside the ferrite. After annealing for 10 000 h, the number density of the G-phase within the 2205 alloy was found to be roughly one order of magnitude higher than in the 2101 alloy. The G-phase precipitates have an additional deleterious effect on the thermal embrittlement, as evaluated by the Ashby–Orowan equation, which explains the discrepancy between the hardness and the rate of phase separation with respect to annealing time (Gladman T 1999 Mater. Sci. Tech. Ser. 15 30–36).

36 MATERIALS SCIENCE↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Estimating the density of states of frustrated spin systems

Abstract Estimating the density of states (DOS) of systems with rugged free energy landscapes is a notoriously difficult task of the utmost importance in many areas of physics ranging from spin glasses to biopolymers. DOS estimation has also recently become an indispensable tool for the benchmarking of quantum annealers when these function as samplers. Some of the standard approaches suffer from a spurious convergence of the estimates to metastable minima, and these cases are particularly hard to detect. Here, we introduce a sampling technique based on population annealing enhanced with a multi-histogram analysis and report on its performance for spin glasses. We demonstrate its ability to overcome the pitfalls of other entropic samplers, resulting in some cases in large scaling advantages that can lead to the uncovering of new physics. The new technique avoids some inherent difficulties in established approaches and can be applied to a wide range of systems without relevant tailoring requirements. Benchmarking of the studied techniques is facilitated by the introduction of several schemes that allow us to achieve exact counts of the degeneracies of the tested instances.

Barash, Lev (ORCID:000000022298785X)↗
DOE OSTI2022

Organizing Large Data Sets for Efficient Analyses on HPC Systems

Upcoming exascale applications could introduce significant data management challenges due to their large sizes, dynamic work distribution, and involvement of accelerators such as graphical processing units, GPUs. In this work, we explore the performance of reading and writing operations involving one such scientific application on two different supercomputers. Our tests showed that the Adaptable Input and Output System, ADIOS, was able to achieve speeds over 1TB/s, a significant fraction of the peak I/O performance on Summit. We also demonstrated the querying functionality in ADIOS could effectively support common selective data analysis operations, such as conditional histograms. In tests, this query mechanism was able to reduce the execution time by a factor of five. More importantly, ADIOS data management framework allows us to achieve these performance improvements with only a minimal amount of coding effort.

Gu, Junmin↗
DOE OSTI2014

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

At the mass scale of a proton, the strong force is not well understood. Various quark models exist, but it is important to determine which quark model(s) are most accurate. Experimentally, finding resonances predicted by some models and not others would give valuable insight into this fundamental interaction. Several labs around the world use photoproduction experiments to find these missing resonances. The aim of this work is to develop a robust Bayesian data analysis program for extracting polarisation observables from pseudoscalar meson photoproduction experiments using CLAS at Jefferson Lab. This method, known as nested sampling, has been compared to traditional methods and has incorporated data parallelisation and GPU programming. It involves an event-by-event likelihood function, which has no associated loss of information from histogram binning, and results can be easily constrained to the physical region. One of the most important advantages of the nested sampling approach is that data from different experiments can be combined and analysed simultaneously. Results on both simulated and previously analysed experimental data for the K{sup +}Λ channel will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Open access to high-level data and analysis tools in the CMS experiment at the LHC

The CMS experiment, in recognition of its commitment to data preservation and open access as well as to education and outreach, has made its first public release of high-level data under the CC0 waiver: up to half of the proton-proton collision data (by volume) at 7 TeV from 2010 in CMS Analysis Object Data format. CMS has prepared, in collaboration with CERN and the other LHC experiments, an open-data web portal based on Invenio. The portal provides access to CMS public data as well as to analysis tools and documentation for the public. The tools include an event display and histogram application that run in the browser. In addition a virtual machine containing a CMS software environment along with XRootD access to the data is available. Within the virtual machine the public can analyse CMS data, example code is provided. As a result, we describe the accompanying tools and documentation and discuss the first experiences of data use.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

IDENTIFYING POTENTIAL MARKERS OF THE SUN'S GIANT CONVECTIVE SCALE

Line-of-sight magnetograms from the Helioseismic and Magnetic Imager (HMI) of the Solar Dynamics Observatory (SDO) are analyzed using a diagnostic known as the magnetic range of influence (MRoI). The MRoI is a measure of the length over which a photospheric magnetogram is balanced and so its application gives the user a sense of the connective length scales in the outer solar atmosphere. The MRoI maps and histograms inferred from the SDO/HMI magnetograms primarily exhibit four scales: a scale of a few megameters that can be associated with granulation, a scale of a few tens of megameters that can be associated with super-granulation, a scale of many hundreds to thousands of megameters that can be associated with coronal holes and active regions, and a hitherto unnoticed scale that ranges from 100 to 250 Mm. We infer that this final scale is an imprint of the (rotationally driven) giant convective scale on photospheric magnetism. This scale appears in MRoI maps as well-defined, spatially distributed concentrations that we have dubbed ''g-nodes''. Furthermore, using coronal observations from the Atmospheric Imaging Assembly on SDO, we see that the vicinity of these g-nodes appears to be a preferred location for the formation of extreme-ultraviolet (and likely X-Ray) brightpoints. These observations and straightforward diagnostics offer the potential of a near real-time mapping of the Sun's largest convective scale, a scale that possibly reaches to the very bottom of the convective zone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Molecular counting by photobleaching in protein complexes with many subunits: best practices and application to the cellulose synthesis complex

The constituents of large, multisubunit protein complexes dictate their functions in cells, but determining their precise molecular makeup in vivo is challenging. One example of such a complex is the cellulose synthesis complex (CSC), which in plants synthesizes cellulose, the most abundant biopolymer on Earth. In growing plant cells, CSCs exist in the plasma membrane as six-lobed rosettes that contain at least three different cellulose synthase (CESA) isoforms, but the number and stoichiometry of CESAs in each CSC are unknown. To begin to address this question, we performed quantitative photobleaching of GFP-tagged AtCESA3-containing particles in living Arabidopsis thaliana cells using variable-angle epifluorescence microscopy and developed a set of information-based step detection procedures to estimate the number of GFP molecules in each particle. The step detection algorithms account for changes in signal variance due to changing numbers of fluorophores, and the subsequent analysis avoids common problems associated with fitting multiple Gaussian functions to binned histogram data. The analysis indicates that at least 10 GFP-AtCESA3 molecules can exist in each particle. In conclusion, these procedures can be applied to photobleaching data for any protein complex with large numbers of fluorescently tagged subunits, providing a new analytical tool with which to probe complex composition and stoichiometry.

09 BIOMASS FUELS↗
DOE OSTI2020

Global adjoint tomography—model GLAD-M25

SUMMARY Building on global adjoint tomography model GLAD-M15, we present transversely isotropic global model GLAD-M25, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting of 1480 events in the magnitude range 5.5 ≤ Mw ≤ 7.2, an almost sixfold increase over the first-generation model. We calculated fully 3-D synthetic seismograms with a shortest period of 17 s based on a GPU-accelerated spectral-element wave propagation solver which accommodates effects due to 3-D anelastic crust and mantle structure, topography and bathymetry, the ocean load, ellipticity, rotation and self-gravitation. We used an adjoint-state method to calculate Fréchet derivatives in 3-D anelastic Earth models facilitated by a parsimonious storage algorithm. The simulations were performed on the Cray XK7 ‘Titan’ and the IBM Power 9 ‘Summit’ at the Oak Ridge Leadership Computing Facility. We quantitatively evaluated GLAD-M25 by assessing misfit reductions and traveltime anomaly histograms in 12 measurement categories. We performed similar assessments for a held-out data set consisting of 360 earthquakes, with results comparable to the actual inversion. We highlight the new model for a variety of plumes and subduction zones.

58 GEOSCIENCES↗