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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measurements of effective actinides neutron capture cross sections in a cadmium-filtered epithermal neutron spectrum

In this work, a total of 20 actinide samples were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL): 233 U (1), 235 U (1), 236 U (1), 238 U (1), 237 Np (2), 239 Pu (1), 240 Pu (1), 242 Pu (2), 244 Pu (2), 241 Am (2), 243 Am (2), 244 Cm (2), and 248 Cm (2). The samples were loaded in a basket containing a 1.1-mm thick cadmium filter. The presence of the filter drastically changes the neutron spectrum in the samples and accentuates the reaction rates in the resonance region. Isotopic ratios were determined using Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS) that allowed inferring effective capture cross sections. Detailed best-estimate MCNP models of the experiment using ENDF/B-VII.0 nuclear data were developed to calculate the effective neutron cross sections and, subsequently, compare them with the cross sections inferred from experimental results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Neural network ensembles and uncertainty estimation for predictions of inelastic mechanical deformation using a finite element method-neural network approach

The finite element method (FEM) is widely used to simulate a variety of physics phenomena. Approaches that integrate FEM with neural networks (NNs) are typically leveraged as an alternative to conducting expensive FEM simulations in order to reduce the computational cost without significantly sacrificing accuracy. However, these methods can produce biased predictions that deviate from those obtained with FEM, since these hybrid FEM-NN approaches rely on approximations trained using physically relevant quantities. In this work, an uncertainty estimation framework is introduced that leverages ensembles of Bayesian neural networks to produce diverse sets of predictions using a hybrid FEM-NN approach that approximates internal forces on a deforming solid body. The uncertainty estimator developed herein reliably infers upper bounds of bias/variance in the predictions for a wide range of interpolation and extrapolation cases using a three-element FEM-NN model of a bar undergoing plastic deformation. This proposed framework offers a powerful tool for assessing the reliability of physics-based surrogate models by establishing uncertainty estimates for predictions spanning a wide range of possible load cases.

42 ENGINEERING↗
DOE OSTI2020

2D Retrieval Frameworks for Hot Jupiter Phase Curves

Spectroscopic phase curves provide unique access to the three-dimensional properties of transiting exoplanet atmospheres. However, a modeling framework must be developed to deliver accurate inferences of atmospheric properties for these complex data sets. Here, we develop an approach to retrieve temperature structures and molecular abundances from phase curve spectra at any orbital phase. In the context of a representative hot Jupiter with a large day–night temperature contrast, we examine the biases in typical one-dimensional (1D) retrievals as a function of orbital phase/geometry, compared to two-dimensional (2D) models that appropriately capture the disk-integrated phase geometry. We guide our intuition by applying our new framework on a simulated Hubble Space Telescope (HST)+Spitzer phase curve data set in which the “truth” is known, followed by an application to the spectroscopic phase curve of the canonical hot Jupiter, WASP-43b. We also demonstrate the retrieval framework on simulated James Webb Space Telescope (JWST) phase curve observations. We apply our new geometric framework to a joint fit of all spectroscopic phases, assuming longitudinal molecular abundance homogeneity, resulting in an a factor of 2 improvement in abundances precision when compared to individual phase constraints. With a 1D retrieval model on simulated HST+Spitzer data, we find strongly biased molecular abundances for CH{sub 4} and CO{sub 2} at most orbital phases. With 2D, the day and night profiles retrieved from WASP-43b remain consistent throughout the orbit. JWST retrievals show that a 2D model is strongly favored at all orbital phases. Based on our new 2D retrieval implementation, we provide recommendations on when 1D models are appropriate and when more complex phase geometries involving multiple TP profiles are required to obtain an unbiased view of tidally locked planetary atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2-24.3 M ⊙ black hole and a compact object with a mass of 2.50-2.67 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to 18.5 deg 2 at a distance of 241 +41-41 Mpc; no electromagnetic counterpart has been confirmed to date. The source has the most unequal mass ratio yet measured with gravitational waves , 0.112+0.0090.008, , and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to ≤0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence. We estimate a merger rate density of 1-23 Gpc -3 yr -1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

We report the observation of a compact binary coalescence involving a 22.2–24.3 Me black hole and a compact object with a mass of 2.50–2.67Me (all measurements quoted at the 90% credible level). The gravitational-wavesignal, GW190814, was observed during LIGO’s and Virgo’s third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network. The source was localized to18.5 deg2 at a distance of-+2414541Mpc; no electromagnetic counterpart has been confirmed to date. The source has the most unequal mass ratio yet measured with gravitational waves,-+0.1120.0090.008, and its secondary component is either the lightest black hole or the heaviest neutron star ever discovered in a double compact-object system. The dimensionless spin of the primary black hole is tightly constrained to 0.07. Tests of general relativity reveal no measurable deviations from the theory, and its prediction of higher-multipole emission is confirmed at high confidence. We estimate a merger rate density of 1–23 Gpc−3yr−1 for the new class of binary coalescence sources that GW190814 represents. Astrophysical models predict that binaries with mass ratios similar to this event can form through several channels, but are unlikely to have formed in globular clusters. However, the combination of mass ratio, component masses, and the inferred merger rate for this event challenges all current models of the formation and mass distribution of compact-object binaries.

Gravitational wave astronomy↗
DOE OSTI2023

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, $R_0$. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific $R_0$ values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for $R_0$ range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Improved Statistical Analysis for the Neutrinoless Double-Beta Decay Matrix Element of 136Xe

Neutrinoless double beta decay nuclear matrix element (M0ν) for 136Xe was recently analyzed using a statistical approach (Phys. Rev. C 107, 045501 (2023)). In the analysis, three initial shell model effective Hamiltonians were randomly altered, and their results for 23 measured observables were used to infer credibility for the M0ν nuclear matrix element (NME) based on a Bayesian Model Averaging approach. In that analysis, a reasonable Gamow-Teller quenching factor of 0.7 was assumed for each starting effective Hamiltonian. Given that the result of the statistical analysis was sensible to this choice, we are here improving that analysis by assuming that the Gamow-Teller quenching factor is also randomly chosen within reasonabe limits for all three starting Hamiltonians. The outcomes are slightly higher expectation values and uncertainties for the M0ν NME.

Astronomy & Astrophysics↗
DOE OSTI2016

ENSO-driven energy budget perturbations in observations and CMIP models

Various observation-based datasets are employed to robustly quantify changes in ocean heat content (OHC), anomalous ocean–atmosphere energy exchanges and atmospheric energy transports during El Niño-Southern Oscillation (ENSO). These results are used as a benchmark to evaluate the energy pathways during ENSO as simulated by coupled climate model runs from the CMIP3 and CMIP5 archives. The models are able to qualitatively reproduce observed patterns of ENSO-related energy budget variability to some degree, but key aspects are seriously biased. Area-averaged tropical Pacific OHC variability associated with ENSO is greatly underestimated by all models because of strongly biased responses of net radiation at top-of-the-atmosphere to ENSO. The latter are related to biases of mean convective activity in the models and project on surface energy fluxes in the eastern Pacific Intertropical Convergence Zone region. Moreover, models underestimate horizontal and vertical OHC redistribution in association with the generally too weak Bjerknes feedback, leading to a modeled ENSO affecting a too shallow layer of the Pacific. Vertical links between SST and OHC variability are too weak even in models driven with observed winds, indicating shortcomings of the ocean models. Furthermore, modeled teleconnections as measured by tropical Atlantic OHC variability are too weak and the tropical zonal mean ENSO signal is strongly underestimated or even completely missing in most of the considered models. In conclusion, results suggest that attempts to infer insight about climate sensitivity from ENSO-related variability are likely to be hampered by biases in ENSO in CMIP simulations that do not bear a clear link to future changes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗
DOE OSTI2019

Estimation of inflow uncertainties in laminar hypersonic double-cone experiments

We propose herein a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inconsistencies. This is crucial in model validation efforts. The framework relies on Bayesian inference to estimate experimental settings deemed uncertain, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements. We test the correctness of the framework on three double-cone experiments conducted in the CUBRC Inc.'s LENS-I shock tunnel, which have also been numerically simulated successfully. Thereafter, we use the framework to investigate two double-cone experiments (executed in the LENS-XX shock tunnel) which have encountered difficulties when used in model validation exercises. We detect an inconsistency with one of the LENS-XX experiments. In addition, we hypothesize two causes for our inability to simulate LEXS-XX experiments accurately and test them using our framework. We find that there is no single cause that explains all the discrepancies between model predictions and experimental data, but different causes explain different discrepancies, to larger or smaller extent. We end by proposing that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and we cite three different methods to do so, the third of which we present in this paper.

Thermophysics↗
NASA NTRS1994

Secular evolution of the vertical column abundances of CHClF2 (HCFC-22) in the Earth's atmosphere inferred from ground-based IR solar observations at the Jungfraujoch and at Kitt Peak, and comparison with model calculations

Series of high-resolution infrared solar spectra recorded at the International Scientific Station of the Jungfraujoch, Switzerland, between 06/1986 and 11/1992, and at Kitt Peak National Observatory, Tucson, Arizona (U.S.A.), from 12/1980 to 04/1992, have been analyzed to provide a comprehensive ensemble of vertical column abundances of CHClF2 (HCFC-22; Freon-22) above the European and the North American continents. The columns were derived from nonlinear least-squares curve fittings between synthetic spectra and the observations containing the unresolved 2 nu(sub 6) Q-branch absorption of CHClF2 at 829.05/cm. The changes versus time observed in these columns were modeled assuming both an exponential and a linear increase with time. The exponential rates of increase at one-sigma uncertainties were found equal to (7.0 +/- 0.35)%/yr for the Junfraujoch data and (7.0 +/- 0.23)%/yr for the Kitt Peak data. The exponential trend of 7.0%/yr found at both stations widely separated in location can be considered as representative of the global increase of the CHClF2 burden in the Earth's atmosphere during the period 1980 to 1992. When assuming two realistic vertical volume mixing ratio profiles for CHClF2 in the troposphere, one quasi constant and the other decreasing by about 13% from the ground to the tropopause, the concentrations for mid-1990 were found to lie between 97 and 111 pptv (parts per trillion by volume) at the 3.58 km altitude of the Jungfraujoch and between 97 and 103 pptv at Kitt Peak, 2.09 km above sea level. Corresponding values derived from calculations using a high vertical resolution-2D model and recently compiled HCFC-22 releases to the atmosphere, were equal to 107 and 105 pptv, respectively, in excellent agreement with the measurements. The model calculated lifetime of CHClF2 was found equal to 15.6 years. The present results are compared critically with similar data found in the literature. On average, the concentrations found here are lower by 15-20% than those derived from in situ investigations; this difference cannot be explained by the absolute uncertainty of +/- 11% assigned presently to the infrared remote measurements.

Zander, R.↗
DOE OSTI2015

THE NON-UNIVERSALITY OF THE LOW-MASS END OF THE IMF IS ROBUST AGAINST THE CHOICE OF SSP MODEL

We perform a direct comparison of two state-of-the art single stellar population (SSP) models that have been used to demonstrate the non-universality of the low-mass end of the initial mass function (IMF) slope. The two public versions of the SSP models are restricted to either solar abundance patterns or solar metallicity, too restrictive if one aims to disentangle elemental enhancements, metallicity changes, and IMF variations in massive early-type galaxies (ETGs) with star formation histories different from those in the solar neighborhood. We define response functions (to metallicity and α-abundance) to extend the parameter space for each set of models. We compare these extended models with a sample of Sloan Digital Sky Survey (SDSS) ETG spectra with varying velocity dispersions. We measure equivalent widths of optical IMF-sensitive stellar features to examine the effect of the underlying model assumptions and ingredients, such as stellar libraries or isochrones, on the inference of the IMF slope down to ∼0.1 M{sub ⊙}. We demonstrate that the steepening of the low-mass end of the IMF based on a non-degenerate set of spectroscopic optical indicators is robust against the choice of the stellar population model. Although the models agree in a relative sense (i.e., both imply more bottom-heavy IMFs for more massive systems), we find non-negligible differences in the absolute values of the IMF slope inferred at each velocity dispersion by using the two different models. In particular, we find large inconsistencies in the quantitative predictions of the IMF slope variations and abundance patterns when sodium lines are used. We investigate the possible reasons for these inconsistencies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Machine Learning Models for Network Traffic Classification in Programmable Logic

Network traffic classification via machine learning on network packet payloads has emerged as an active area of research for network security due to the high accuracy machine learning models have achieved in classifying payloads. For effective deployment as part of network security, these machine learning models must not only classify malicious packet payloads accurately, they must also identify anomalous payloads and perform inference at speeds generally faster than 10,000 packets per second to be effective. This work explores the in- ference speeds and accuracy of several neural network models implemented in programmable logic on various field programmable gate arrays (FPGA) including the Xilinx VC1902 and Xilinx Zynq Ultrascale+. This work also presents the design and performance of both an autoencoder and variational autoencoder programmed on the FPGA for identifying anomalous packet payloads. The performance benefits of the FPGA implementation for this type of packet payload inspection driven by machine learning are compared against graphics processing unit (GPU) inference implementations run on two state-of-the-art datacenter GPU devices, the NVIDIA V100 and A100. The model accuracy difference between the FPGA and GPU implementations was found to be 4% or less while the Xilinx VC1902 outperformed both the NVIDIA V100 and A100 for inference speeds on all the models explored except the variational autoencoder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

REV-INR: Regularized Evidential Implicit Neural Representation for Uncertainty-Aware Volume Visualization

Applications of Implicit Neural Representations (INRs) have emerged as a promising deep learning approach for compactly representing large volumetric datasets. These models can act as surrogates for volume data, enabling efficient storage and on-demand reconstruction via model predictions. However, conventional deterministic INRs only provide value predictions without insights into the model’s prediction uncertainty or the impact of inherent noisiness in the data. This limitation can lead to unreliable data interpretation and visualization due to prediction inaccuracies in the reconstructed volume. Identifying erroneous results extracted from model-predicted data may be infeasible, as raw data may be unavailable due to its large size. To address this challenge, we introduce REV-INR, Regularized Evidential Implicit Neural Representation, which learns to predict data values accurately along with the associated coordinate-level data uncertainty and model uncertainty using only a single forward pass of the trained REV-INR during inference. By comprehensively comparing and contrasting REV-INR with existing well-established deep uncertainty estimation methods, we show that REV-INR achieves the best volume reconstruction quality with robust data (aleatoric) and model (epistemic) uncertainty estimates using the fastest inference time. Consequently, we demonstrate that REV-INR facilitates assessment of the reliability and trustworthiness of the extracted isosurfaces and volume visualization results, enabling analyses to be solely driven by model-predicted data.

Saklani, Shanu [Indian Institute of Technology, Ka↗
DOE OSTI2022

Graph link prediction in computer networks using Poisson matrix factorisation

Graph link prediction is an important task in cybersecurity: relationships between entities within a computer network, such as users interacting with computers or system libraries and the corresponding processes that use them, can provide key insights into adversary behaviour. Poisson matrix factorisation (PMF) is a popular model for link prediction in large networks, particularly useful for its scalability. In this article PMF is extended to include scenarios that are commonly encountered in cybersecurity applications. Specifically, an extension is proposed to explicitly handle binary adjacency matrices and include known categorical covariates associated with the graph nodes. A seasonal PMF model is also presented to handle seasonal networks. To allow the methods to scale to large graphs, variational methods are discussed for performing fast inference. The results show an improved performance over the standard PMF model and other statistical network models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bayesian projection pursuit regression

In projection pursuit regression (PPR), a univariate response variable is approximated by the sum of $M$ “ridge functions,” which are flexible functions of one-dimensional projections of a multivariate input variable. Traditionally, optimization routines are used to choose the projection directions and ridge functions via a sequential algorithm, and $M$ is typically chosen via cross-validation. Here, we introduce a novel Bayesian version of PPR, which has the benefit of accurate uncertainty quantification. To infer appropriate projection directions and ridge functions, we apply novel adaptations of methods used for the single ridge function case ($M$=1), called the Bayesian Single Index Model; and use a Reversible Jump Markov chain Monte Carlo algorithm to infer the number of ridge functions $M$. We evaluate the predictive ability of our model in 20 simulated scenarios and for 23 real datasets, in a bake-off against an array of state-of-the-art regression methods. Finally, we generalize this methodology and demonstrate the ability to accurately model multivariate response variables. Its effective performance indicates that Bayesian Projection Pursuit Regression is a valuable addition to the existing regression toolbox.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Azimuthal asymmetry in the risetime of the surface detector signals of the Pierre Auger Observatory

The azimuthal asymmetry in the risetime of signals in Auger surface detector stations is a source of information on shower development. The azimuthal asymmetry is due to a combination of the longitudinal evolution of the shower and geometrical effects related to the angles of incidence of the particles into the detectors. The magnitude of the effect depends upon the zenith angle and state of development of the shower and thus provides a novel observable, (secθ) max , sensitive to the mass composition of cosmic rays above 3 x 10 18 eV. By comparing measurements with predictions from shower simulations, we find for both of our adopted models of hadronic physics (QGSJETII-04 and EPOS-LHC) an indication that the mean cosmic-ray mass increases slowly with energy, as has been inferred from other studies. However, the mass estimates are dependent on the shower model and on the range of distance from the shower core selected. Furthermore, the method has uncovered further deficiencies in our understanding of shower modelling that must be resolved before the mass composition can be inferred from (secθ) max .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Yield strength dependence on strain rate of molybdenum-alloy nanofibers

The yield strength dependence on strain rate was studied for molybdenum-alloy nanofibers with varying initial dislocation density at three different pre-strain levels. In-situ tensile experiments at three displacement rates were carried out in a scanning electron microscope. Yield strength and its scatter decreased as a function of the pre-strain level for different displacement rates. A statistical model was used to analyze the results, and a negative strain rate dependence was inferred from the yield experiments. This finding suggests the need for theoretical investigations since classical models such as dynamic strain aging may have limitations at such nanoscales.

36 MATERIALS SCIENCE↗