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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Statistical description of coalescing magnetic islands via magnetic reconnection

The physical picture of interacting magnetic islands provides a useful paradigm for certain plasma dynamics in a variety of physical environments, such as the solar corona, the heliosheath and the Earth's magnetosphere. In this work, we derive an island kinetic equation to describe the evolution of the island distribution function (in area and in flux of islands) subject to a collisional integral designed to account for the role of magnetic reconnection during island mergers. This equation is used to study the inverse transfer of magnetic energy through the coalescence of magnetic islands in two dimensions. We solve our island kinetic equation numerically for three different types of initial distribution: Dirac delta, Gaussian and power-law distributions. The time evolution of several key quantities is found to agree well with our analytical predictions: magnetic energy decays as $\tilde {t}^{-1}$ , the number of islands decreases as $\tilde {t}^{-1}$ and the averaged area of islands grows as $\tilde {t}$ , where $\tilde {t}$ is the time normalised to the characteristic reconnection time scale of islands. General properties of the distribution function and the magnetic energy spectrum are also studied. Finally, we discuss the underlying connection of our island-merger models to the (self-similar) decay of magnetohydrodynamic turbulence.

Physics↗

DOE OSTI2026

Gaussian processes for inferring parton distributions

The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results. We study a framework based on Gaussian Process Regression (GPR) to reconstruct PDFs from lattice QCD matrix elements. Within a Bayesian framework, Gaussian processes serve as flexible priors that encode uncertainties, correlations, and constraints without imposing rigid functional forms. We investigate a wide range of kernel choices, mean functions, and hyperparameter treatments. We quantify information gained from the data using the Kullback-Leibler divergence. Synthetic data tests demonstrate the consistency and robustness of the method. Our study establishes GPR as a systematic and non-parametric approach to PDF reconstruction, offering controlled uncertainty estimates and reduced model bias in lattice QCD analyses.

hadronic spectroscopy↗

NASA NTRS1995

Gaussian statistics of the cosmic microwave background: Correlation of temperature extrema in the COBE DMR two-year sky maps

We use the two-point correlation function of the extrema points (peaks and valleys) in the Cosmic Background Explorer (COBE) Differential Microwave Radiometers (DMR) 2 year sky maps as a test for non-Gaussian temperature distribution in the cosmic microwave background anisotropy. A maximum-likelihood analysis compares the DMR data to n = 1 toy models whose random-phase spherical harmonic components a(sub lm) are drawn from either Gaussian, chi-square, or log-normal parent populations. The likelihood of the 53 GHz (A+B)/2 data is greatest for the exact Gaussian model. There is less than 10% chance that the non-Gaussian models tested describe the DMR data, limited primarily by type II errors in the statistical inference. The extrema correlation function is a stronger test for this class of non-Gaussian models than topological statistics such as the genus.

Kogut, A.↗

DOE OSTI2021

Global Sensitivity Analysis of Large Distribution System with PVs using Deep Gaussian Process

Global sensitivity analysis (GSA) of the voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows us to resort to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with a different number and different probability distributions of uncertain sources demonstrate that the proposed method can achieve accurate GSA under various conditions.

14 SOLAR ENERGY↗

DOE OSTI2025

Switching kinetics in ferroelectric zinc magnesium oxide thin films

The switching kinetics of RF-magnetron reactively sputtered ~200 nm thick Zn 1-x Mg x O (ZMO) ferroelectric thin films with x = 0.41 and x = 0.27 prepared on Pt/Ti/SiO 2 /Si substrates were studied at applied fields near the coercive fields, ranging from 3.4 to 5.1 MV cm -1 , and at temperatures ranging from room temperature to 100 °C. Polarization reversal in ZMO followss the Kolmogorov-Avrami-Ishibashi kinetics model for nucleation and growth at applied fields near the coercive field, and obeys an individual column switching (ICS) model at higher applied fields required to switch most of the spontaneous polarization. Switching in the high-applied field regime can be described using either the Gaussian or inverse gamma distribution functions depending on the mole fraction of magnesium in the film. The switching current transients of the high-Mg content ZMO film (x = 0.41) are always bi-modal, whereas the low Mg composition film (x = 0.27) is described by the Gaussian distribution initially following wake-up, and becomes bimodal with continued cycling. Rayleigh-like behavior of the dielectric constant revealed a simultaneous increase of the irreversible and decrease of the reversible contributions to the dielectric constant, which was ascribed to an increase in the density of mobile domain walls with cycling and resulted in faster switching.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2026

The Poisson tensor completion parametric estimator

We introduce the Poisson tensor completion (PTC) estimator that exploits inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram for samples of a multivariate distribution. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial non-homogeneous Poisson process. The Poisson tensor decomposition leads to a completion of the mean measure over all bins—including those containing few to no samples—and leads to our proposed estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values obviating the need for additional constraints to ensure non-negativity. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗

DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗

NASA NTRS1981

Effect of Surface Roughness on Hydrodynamic Bearings

A theoretical analysis on the performance of hydrodynamic oil bearings is made considering surface roughness effect. The hydrodynamic as well as asperity contact load is found. The contact pressure was calculated with the assumption that the surface height distribution was Gaussian. The average Reynolds equation of partially lubricated surface was used to calculate hydrodynamic load. An analytical expression for average gap was found and was introduced to modify the average Reynolds equation. The resulting boundary value problem was then solved numerically by finite difference methods using the method of successive over relaxation. The pressure distribution and hydrodynamic load capacity of plane slider and journal bearings were calculated for various design data. The effects of attitude and roughness of surface on the bearing performance were shown. The results are compared with similar available solution of rough surface bearings. It is shown that: (1) the contribution of contact load is not significant; and (2) the hydrodynamic and contact load increase with surface roughness.

Majumdar, B. C.↗

DOE OSTI2021

A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity

We present a stochastic mathematical model of the intracellular infection dynamics of Bacillus anthracis in macrophages. Following inhalation of B. anthracis spores, these are ingested by alveolar phagocytes. Ingested spores then begin to germinate and divide intracellularly. This can lead to the eventual death of the host cell and the extracellular release of bacterial progeny. Some macrophages successfully eliminate the intracellular bacteria and will recover. Here, a stochastic birth-and-death process with catastrophe is proposed, which includes the mechanism of spore germination and maturation of B. anthracis . The resulting model is used to explore the potential for heterogeneity in the spore germination rate, with the consideration of two extreme cases for the rate distribution: continuous Gaussian and discrete Bernoulli. We make use of approximate Bayesian computation to calibrate our model using experimental measurements from in vitro infection of murine peritoneal macrophages with spores of the Sterne 34F2 strain of B. anthracis . The calibrated stochastic model allows us to compute the probability of rupture, mean time to rupture, and rupture size distribution, of a macrophage that has been infected with one spore. We also obtain the mean spore and bacterial loads over time for a population of cells, each assumed to be initially infected with a single spore. Our results support the existence of significant heterogeneity in the germination rate, with a subset of spores expected to germinate much later than the majority. Furthermore, in agreement with experimental evidence, our results suggest that most of the spores taken up by macrophages are likely to be eliminated by the host cell, but a few germinated spores may survive phagocytosis and lead to the death of the infected cell. Finally, we discuss how this stochastic modelling approach, together with dose-response data, allows us to quantify and predict individual infection risk following exposure.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1973

Depolarization of an uncollimated laser beam in a turbulent medium

The depolarization of a beam wave, with Gaussian intensity distribution and arbitrary divergence, propagating in a turbulent medium is considered. The correlation function of the depolarized component of the beam is determined. The mean-square depolarization fluctuation is then determined for a medium characterized by a Kolmogorov spectrum. If the beam is assumed to be collimated, this expression reduces to that of Collett and Alferness; if the plane-wave limiting case of the Gaussian beam is taken, the results in this paper agree with those of Strohbehn. The results show that off the beam axis the depolarization can be greater than for the plane wave, and that near the beam focal point the depolarization may decrease by orders of magnitude.

Chang, F. C.↗

NASA NTRS1986

Modeling of infrared flux spectra from disk-shaped interstellar dust clouds

Although there is growing observational and theoretical evidence for many disk-shaped objects of astrophysical interest, spherical geometry is assumed in most radiative transfer models. Recently we generalized the quasi-diffusion method developed by Leung (1975, 1976) for spherical geometry to solve the problem of scattering, absorption, and reemission by dust grains in a medium of disk geometry. The method is applicable to a variety of astronomical sources whose dynamics are angular-momentum dominated and hence not accurately treated by spherical geometry, e.g., protoplanetary nebulae, circumstellar disks, bipolar-flow molecular clouds, accretion disks and disk galaxies. Using this technique and realistic grain opacities we construct theoretical models to determine self-consistently the dust temperature distribution and infrared emission from disk-shaped, quiescent dark globules heated externally by the intersteller radiation field. The effects of the following parameters on the temperature structure and the emergent spectrum are studied: grain type (graphite and silicate), optical depth, density inhomogeneity, and degree of disk flattening. The disk models are characterized by an aspect ratio and an optical depth at 0.55 microns. For inhomogeneous models, a gaussian density distribution is assumed such that the ratio of central to surface density is 100. To study the effects of source geometry, we also compare results for models with spherical and disk geometry. In this case both models have the same radius and central optical depth, the disk models having a 1:1 aspect ratio. While the dust temperature distributions in the two cases are very similar, the emergent flux for the disk model depends sensitively on the viewing angle. For clouds which are unresolved, one would expect, since the thermal emission is isotropic in the neighborhood of an emitting grain, and since the emission (in the far infrared) is optically thin, that the emergent flux spectrum should be characteristic only of the dust temperature, and independent of viewing angle. However, because of the lack of complete symmetry and the resulting radiation anisotropy, this is found not to be the case for the disk models. Angle is that it implies a large uncertainty in estimating the radiation dust mass.

Spagna, G. F., Jr.↗

NASA NTRS1973

Determination of Atmospheric Aerosol Characteristics from the Polarization of Scattered Radiation

Aerosols affect the polarization of radiation in scattering, hence measured polarization can be used to infer the nature of the particles. Size distribution, particle shape, real and absorption parts of the complex refractive index affect the scattering. From Lorenz-Mie calculations of the 4-Stokes parameters as a function of scattering angle for various wavelengths the following polarization parameters were plotted: total intensity, intensity of polarization in plane of observation, intensity perpendicular to the plane of observation, polarization ratio, polarization (using all 4-Stokes parameters), plane of the polarization ellipse and its ellipticity. A six-component log-Gaussian size distribution model was used to study the effects of the nature of the polarization due to variations in the size distribution and complex refractive index. Though a rigorous inversion from measurements of scattering to detailed specification of aerosol characteristics is not possible, considerable information about the nature of the aerosols can be obtained. Only single scattering from aerosols was used in this paper. Also, the background due to Rayleigh gas scattering, the reduction of effects as a result of multiple scattering and polarization effects of possible ground background (airborne platforms) were not included.

Harris, F. S., Jr.↗

DOE OSTI2025

The Poisson tensor completion non-parametric differential entropy estimator

We introduce the Poisson tensor completion (PTC) estimator, a non-parametric differential entropy estimator. The PTC estimator leverages inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial Poisson process. The Poisson tensor decomposition leads to a completion of the intensity measure over all bins—including those containing few to no samples—and leads to our proposed PTC differential entropy estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values and so can be safely used directly in entropy estimation. Our estimator is the first tensor-based estimator that exploits the underlying spatial Poisson process related to the histogram explicitly when estimating the probability density with low-rank tensor decompositions for the purpose of tensor completion. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

42 ENGINEERING↗

NASA NTRS2012

Statistical Orbit Determination using the Particle Filter for Incorporating Non-Gaussian Uncertainties

The tracking of space objects requires frequent and accurate monitoring for collision avoidance. As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full probability density function (PDF) of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. Moreover, unmodeled dynamics in the orbit model could introduce non-Gaussian errors into the process noise. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. The PF is applied in the estimation and propagation of a highly eccentric orbit and the results are compared to the Extended Kalman Filter and Splitting Gaussian Mixture algorithms to demonstrate its proficiency.

Mashiku, Alinda↗

NASA NTRS1985

The extent of the local hi halo

Forty-five high-latitude, OB stars have been observed in the Ly alpha and 21 cm lines of HI in an effort to map out the vertical distribution and extent of the local HI halo. The 25 stars for which a reliable HI colum density can be obtained from Ly alpha lie between 60 and 3100 pc from the plane. The principal result is that the total column density of HI at z 1 kpc is, on the average, 5 + or - 3 x 10 the 19th power/sq cm, or 15% of the total sub HI. At relatively low z the data toward some stars suggest a low effective scale height and fairly high average foreground density, while toward others the effective scale height is large and the average density is low. This can be understood as the result of irregularities in the interstellar medium. A model with half of the HI mass in clouds having radii of a few pc and a Gaussian vertical distribution with sigma sub 2 = 135 pc, and half of the mass in an exponential component with a scale height of 500 pc, gives a satisfactory fit to the data. The technique of comparing Ly alpha and 21 cm column densities is also used to discuss the problem of estimating the distance to several possibly subluminous stars.

Lockman, F. J.↗

NASA NTRS1986

The extent of the local H I halo

Forty-five high-latitude, OB stars have been observed in the Ly alpha and 21 cm lines of HI in an effort to map out the vertical distribution and extent of the local HI halo. The 25 stars for which a reliable HI column density can be obtained from Ly alpha lie between 60 and 3100 pc from the plane. The principal result is that the total column density of HI at an absolute value of z greater than 1 kpc is, on the average, 5 + or - 3 x 10 to the 19th power/sq cm, or 15 percent of the total sub HI. At relatively low z the data toward some stars suggest a low effective scale height and fairly high average foreground density, while toward others the effective scale height is large and the average density is low. This can be understood as the result of irregularities in the interstellar medium. A model with half of the HI mass in clouds having radii of a few pc and a Gaussian vertical distribution with sigma sub 2 = 135 pc, and half of the mass in an exponential component with a scale height of 500 pc, gives a satisfactory fit to the data. The technique of comparing Ly alpha and 21 cm column densities is also used to discuss the problem of estimating the distance to several possibly subluminous stars.

Lockman, F. J.↗

NASA NTRS1993

The velocity dispersion of the caustic network due to random motion of individual stars in the lensing galaxy

We present a method of computing the velocity distribution of the caustic network due to the random motion of stars in the lensing galaxy. This method is illustrated on the example of the two-point mass lens and then applied to a large sample of stars. We conclude that the proper motion of the stars increases significantly the frequency of the high magnification events in comparison with a static lens configuration with constant stream or constant bulk velocity. The stream velocity is the velocity of the star field relative to the global bulk velocity of the galaxy. We show that the global bulk and the stream velocity of the star field have to be considered separately for any microlensing situation. The higher the surface mass density of the stars in the lensing galaxy, the higher the influence of proper motion of stars on the statistics of high magnification events. The influence of a Gaussian velocity distribution of the stars in the lensing galaxy compared with a constant stream velocity of the stars increases the number of high magnification events by a factor 1.30 +/- 0.06 for a normalized surface density of the stars cr = 0.1 and by a factor 1.7 +/- 0.1 for sigma = 0.5. This means that for some microlensing situations the proper motion of the stars in a lensing galaxy has to be considered for exact microlensing predictions.

Kundik, Tomislav↗