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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Spacewatch Survey of the Solar System

The purpose of the Spacewatch project is to explore the various populations of small objects throughout the solar system. Statistics on all classes of small bodies are needed to infer their physical and dynamical evolution. More Earth Approachers need to be found to assess the impact hazard. (We have adopted the term "Earth Approacher", EA, to include all those asteroids, nuclei of extinct short period comets, and short period comets that can approach close to Earth. The adjective "near" carries potential confusion, as we have found in communicating with the media, that the objects are always near Earth, following it like a cloud.) Persistent and voluminous accumulation of astrometry of incidentally observed main belt asteroids MBAs will eventually permit the Minor Planet Center (MPQ to determine the orbits of large numbers (tens of thousands) of asteroids. Such a large body of information will ultimately allow better resolution of orbit classes and the determinations of luminosity functions of the various classes, Comet and asteroid recoveries are essential services to planetary astronomy. Statistics of objects in the outer solar system (Centaurs, scattered-disk objects, and Trans-Neptunian Objects; TNOs) ultimately will tell part of the story of solar system evolution. Spacewatch led the development of sky surveying by electronic means and has acted as a responsible interface to the media and general public on this discipline and on the issue of the hazard from impacts by asteroids and comets.

McMillan, Robert S.↗
DOE OSTI2015

Bootstrap performance profiles in stochastic algorithms assessment

Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Making inertial confinement fusion models more predictive

Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in the laboratory. Building truly predictive models is a significant challenge, with the potential to greatly accelerate progress to high yield and ignition. One path to more predictive models is to use experimental data to update the underlying physics in a way that can be extrapolated to new experiments and regimes. We describe a statistical framework for the calibration of ICF simulations using data collected at the National Ignition Facility (NIF). We perform Bayesian inferences for a series of laser shots using an approach that is designed to respect the physics simulation as much as possible and then build a second model that links the individual-shot inferences together. We show that this approach is able to match multiple X-ray and neutron diagnostics for a whole series of NIF “BigFoot” shots. Within the context of 2D radiation hydrodynamic simulations, our inference strongly favors a significant reduction in fuel compression over other known degradation mechanisms (namely, hohlraum issues and engineering perturbations). This analysis is expanded using a multifidelity technique to pick fuel-ablator mix from several candidate causes of the degraded fuel compression (including X-ray preheat and shock timing errors). Finally, we use our globally calibrated model to investigate the extra laser drive energy that would be required to overcome the inferred fuel compression issues in NIF BigFoot implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Automating galaxy morphology classification using k -nearest neighbours and non-parametric statistics

ABSTRACT Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k-nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of ${\sim} 80\!-\!90 \, {{\rm per\, cent}}$ per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible.

Mukundan, Kavya↗
DOE OSTI2023

Model averaging approaches to data subset selection

Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subsets should be weighted. We compare the two criteria closely in a unified treatment based on the Kullback-Leibler divergence and conclude that one of them is subtly flawed and will tend to yield larger uncertainties due to loss of information. Here, analytical and numerical examples are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Modeling Radiation Detection Using a Mixed-Effects Statistical Model for Background Variation

In this paper, we consider radiation source localization in an urban environment as an inverse problem and use Bayesian parameter estimation techniques to infer the source location and intensity. Since parameter estimation generally requires a large number of model evaluations, we employ a simplified photon transport model, as in a 2017 paper by Stefanescu, et al., to reduce computational requirements. However, the model in the 2017 paper does not account for the physical reality that background radiation varies with position within an urban locale. We incorporate statistical mixed-effects modeling into the simplified transport equation from the Stefanescu, et al. paper to reflect individual variation among the background radiation measurements at multiple detector locations. With our new model, we use a Bayesian DRAM-within-Gibbs technique, which incorporates the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, to estimate the mixed-effects parameters. We note that without adequate prior information, we were unable to simultaneously estimate the set of parameters corresponding to the source intensity, source location, and individual background radiation counts. By calibrating the background in the absence of a source, we obtained narrow prior distributions, which permitted us to jointly infer the source intensity and location and the background radiation distribution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

On-chip generation of Einstein-Podolsky-Rosen states with arbitrary symmetry

We experimentally demonstrate a method for integrated-optical generation of two-photon Einstein-Podolsky-Rosen states featuring arbitrary symmetries. In our setting, we employ detuned directional couplers to impose a freely tailorable phase between the two modes of the state. Our results allow to mimic the quantum random walk statistics of bosons, fermions, and anyons, particles with fractional exchange statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1978

A statistical study of crater-associated wind streaks in the north equatorial zone of Mars

The physical characteristics of all crater-related streaks within the north equatorial zone of Mars (between 0 and 30 deg N) are studied. The survey includes 25% of the Martian surface area and covers the latitude band of major interest to the Viking missions. Nine characteristic parameters are measured for each of 2325 streaks, of which 82.3% are classified as light tone, 8.1% as dark tone, and 9.6% as mixed tone. The location of streaks in the investigated zone is considered together with the indicated wind regimes, and the characteristics of the streaks and of their associated craters are discussed. The results show that: (1) the streaks are not uniformly distributed throughout the north equatorial zone but are strongly concentrated in certain areas; (2) the density of streaks displays a significant latitude variation not apparently related to terrain type between 0 and 30 deg N; (3) the percentage of craters with streaks reaches a maximum between 10 and 15 deg N and falls off significantly toward both the equator and higher latitudes; and (4) the density of streaks correlates well with geological terrain type.

Veverka, J.↗
NASA NTRS1991

Avalanches and the distribution of solar flares

The solar coronal magnetic field is proposed to be in a self-organized critical state, thus explaining the observed power-law dependence of solar-flare-occurrence rate on flare size which extends over more than five orders of magnitude in peak flux. The physical picture that arises is that solar flares are avalanches of many small reconnection events, analogous to avalanches of sand in the models published by Bak and colleagues in 1987 and 1988. Flares of all sizes are manifestations of the same physical processes, where the size of a given flare is determined by the number of elementary reconnection events. The relation between small-scale processes and the statistics of global-flare properties which follows from the self-organized magnetic-field configuration provides a way to learn about the physics of the unobservable small-scale reconnection processes. A simple lattice-reconnection model is presented which is consistent with the observed flare statistics. The implications for coronal heating are discussed and some observational tests of this picture are given.

Lu, Edward T.↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2013

Cluster-continuum quantum mechanical models to guide the choice of anions for Li{sup +}-conducting ionomers

A quantum-mechanical investigation on Li poly(ethylene oxide)-based ionomers was performed in the cluster-continuum solvation model (CCM) that includes specific solvation in the first shell surrounding the cation, all surrounded by a polarizable continuum. A four-state model, including a free Li cation, Li{sup +}-anion pair, triple ion, and quadrupole was used to represent the states of Li{sup +} within the ionomer in the CCM. The relative energy of each state was calculated for Li{sup +} with various anions, with dimethyl ether representing the ether oxygen solvation. The population distribution of Li{sup +} ions among states was estimated by applying Boltzmann statistics to the CCM energies. Entropy difference estimates are needed for populations to better match the true ionomer system. The total entropy change is considered to consist of four contributions: translational, rotational, electrostatic, and solvent immobilization entropies. The population of ion states is reported as a function of Bjerrum length divided by ion-pair separation with/without entropy considered to investigate the transition between states. Predicted concentrations of Li{sup +}-conducting states (free Li{sup +} and positive triple ions) are compared among a series of anions to indicate favorable features for design of an optimal Li{sup +}-conducting ionomer; the perfluorotetraphenylborate anion maximizes the conducting positive triple ion population among the series of anions considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Universal statistics of soft gamma-ray repeating (SGR) bursts

Soft gamma repeater (SGR) bursts are considered as a release of magnetic energy stored in the baryon degrees of freedom of the magnetar crust. It is shown that this interpretation allows all observations of these bursts to be systematized and universal statistical properties to be revealed and explained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Comparative performance of modern digital mammography systems in a large breast screening program

Purpose: To compare physical measures pertaining to image quality among digital mammography systems utilized in a large breast screening program. To examine qualitatively differences in these measures and differences in clinical cancer detection rates between CR and DR among sites within that program. Methods: As part of the routine quality assurance program for screening, field measurements are made of several variables considered to correlate with the diagnostic quality of medical images including: modulation transfer function, noise equivalent quanta, d′ (an index of lesion detectability) and air kerma to allow estimation of mean glandular dose. In addition, images of the mammography accreditation phantom are evaluated. Results: It was found that overall there were marked differences between the performance measures of DR and CR mammography systems. In particular, the modulation transfer functions obtained with the DR systems were found to be higher, even for larger detector element sizes. Similarly, the noise equivalent quanta, d′, and the phantom scores were higher, while the failure rates associated with low signal-to-noise ratio and high dose were lower with DR. These results were consistent with previous findings in the authors’ program that the breast cancer detection rates at sites employing CR technology were, on average, 30.6% lower than those that used DR mammography. Conclusions: While the clinical study was not large enough to allow a statistically powered system-by-system assessment of cancer detection accuracy, the physical measures expressing spatial resolution, and signal-to-noise ratio are consistent with the published finding that sites employing CR systems had lower cancer detection rates than those using DR systems for screening mammography.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

An improved numerical method to compute neutron/gamma deexcitation cascades starting from a high spin state

Numerous nuclear processes involve the deexcitation of a compound nucleus through the emission of several neutrons, gamma-rays and/or conversion electrons. The characteristics of such a deexcitation are commonly derived from a total statistical framework often called “Hauser–Feshbach” method. In this work, we highlight a numerical limitation of this kind of method in the case of the deexcitation of a high spin initial state. To circumvent this issue, an improved technique called the Fluctuating Structure Properties (FSP) method is presented. Two FSP algorithms are derived and benchmarked on the calculation of the total radiative width for a thermal neutron capture on 238 U. We compare the standard method with these FSP algorithms for the prediction of particle multiplicities in the deexcitation of a high spin level of 143 Ba. The gamma multiplicity turns out to be very sensitive to the numerical method. The bias between the two techniques can reach 1.5 γγ/cascade. Lastly, the uncertainty of these calculations coming from the lack of knowledge on nuclear structure is estimated via the FSP method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2002

A New Statistically based Autoconversion rate Parameterization for use in Large-Scale Models

The autoconversion rate is a key process for the formation of precipitation in warm clouds. In climate models, physical processes such as autoconversion rate, which are calculated from grid mean values, are biased, because they do not take subgrid variability into account. Recently, statistical cloud schemes have been introduced in large-scale models to account for partially cloud-covered grid boxes. However, these schemes do not include the in-cloud variability in their parameterizations. In this paper, a new statistically based autoconversion rate considering the in-cloud variability is introduced and tested in three cases using the Canadian Single Column Model (SCM) of the global climate model. The results show that the new autoconversion rate improves the model simulation, especially in terms of liquid water path in all three case studies.

Lin, Bing↗
NASA NTRS2003

Mesoscale Computational Investigation of Shocked Heterogeneous Materials with Application to Large Impact Craters

The propagation of shock waves through target materials is strongly influenced by the presence of small-scale structure, fractures, physical and chemical heterogeneities. Pre-existing fractures often create craters that appear square in outline (e.g. Meteor Crater). Reverberations behind the shock from the presence of physical heterogeneity have been proposed as a mechanism for transient weakening of target materials. Pre-existing fractures can also affect melt generation. In this study, we are attempting to bridge the gap in numerical modeling between the micro-scale and the continuum, the so-called meso-scale. To accomplish this, we are developing a methodology to be used in the shock physics hydrocode (CTH) using Monte-Carlo-type methods to investigate the shock properties of heterogeneous materials. By comparing the results of numerical experiments at the micro-scale with experimental results and by using statistical techniques to evaluate the performance of simple constitutive models, we hope to embed the effect of physical heterogeneity into the field variables (pressure, stress, density, velocity) allowing us to directly imprint the effects of micro-scale heterogeneity at the continuum level without incurring high computational cost.

Crawford, D. A.↗