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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

BAYESIAN ANALYSIS OF MULTIPLE HARMONIC OSCILLATIONS IN THE SOLAR CORONA

The detection of multiple mode harmonic kink oscillations in coronal loops enables us to obtain information on coronal density stratification and magnetic field expansion using seismology inversion techniques. The inference is based on the measurement of the period ratio between the fundamental mode and the first overtone and theoretical results for the period ratio under the hypotheses of coronal density stratification and magnetic field expansion of the wave guide. We present a Bayesian analysis of multiple mode harmonic oscillations for the inversion of the density scale height and magnetic flux tube expansion under each of the hypotheses. The two models are then compared using a Bayesian model comparison scheme to assess how plausible each one is given our current state of knowledge.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Bayesian analysis of nuclear polarizability corrections to the Lamb shift of muonic H-atoms and He-ions

The extraction of nuclear charge radii from spectroscopy experiments in muonic atoms is currently limited by the large uncertainties associated with the theoretical evaluation of the nuclear polarizability effects. To facilitate calculations, these polarizability corrections are conventionally expressed as an expansion in a dimensionless parameter η, which has been argued in previous literature to hold an approximate value of 0.33 in light-nuclear systems. In this work, we check this claim by doing a Bayesian analysis of the nuclear-polarizability corrections to the Lamb shift in µ 2 H and µ 3 H atoms and in µ 3 He + and µ 4 He + ions at various orders in the η-expansion. As presented here, our analysis supports the claim that η $\ll$ 1 in these systems and finds truncation uncertainties that are similar to the previous estimate, the only exception being the truncation uncertainties in the µ 3 He + ion, which are found to be larger.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Bayesian analysis of the 86 Sr ⁢(𝛼,𝛼) reaction to constrain the 86 Sr ⁢(𝛼,𝑛) cross section at astrophysical energies

The alpha optical model potential (𝛼-OMP ) is a phenomenological approach used to describe elastic scattering where multiple reaction channels are open. It is one of the most critical inputs for the calculation of thermonuclear reaction rates in explosive stellar environments, but uncertainties within the 𝛼-OMP lead to imprecise predictions hindering comparisons between calculations and observations. In order to improve the precision of the 𝛼-OMP, additional nuclear physics data are required. In this paper, a measurement of the 86 Sr (𝛼, 𝛼) elastic scattering cross section at multiple energies is reported. Here, a local optical potential is constructed via a fully Bayesian analysis of the elastic scattering data. The resulting uncertainties on the low-energy cross sections relevant to nuclear astrophysics are then calculated and shown to be on the order of 50%.

59 ≤ A ≤ 89↗
DOE OSTI2016

Bayesian Analysis of RANS Models

This work demonstrates the advantages of using a Bayesian framework to analyze and calibrate BHR3.1, the main mix model under development at LANL and supported by both Science Campaigns and ASC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Testing non-minimally coupled inflation with CMB data: a Bayesian analysis

We use the most recent cosmic microwave background (CMB) data to perform a Bayesian statistical analysis and discuss the observational viability of inflationary models with a non-minimal coupling, ξ, between the inflaton field and the Ricci scalar. We particularize our analysis to two examples of small and large field inflationary models, namely, the Coleman-Weinberg and the chaotic quartic potentials. We find that ( i ) the ξ parameter is closely correlated with the primordial amplitude ; ( ii ) although improving the agreement with the CMB data in the r − n {sub s} plane, where r is the tensor-to-scalar ratio and n {sub s} the primordial spectral index, a non-null coupling is strongly disfavoured with respect to the minimally coupled standard ΛCDM model, since the upper bounds of the Bayes factor (odds) for ξ parameter are greater than 150:1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Bayesian Analysis of Savings from Retrofit Projects

Estimates of savings from retrofit projects depend on statistical models, but because of the complicated analysis required to determine the uncertainty of the estimates, savings uncertainty is not often considered. Numerous simplified methods have been proposed to determine savings uncertainty, but in all but the simplest cases, these methods provide approximate results only. The objective of this paper is to show that Bayesian inference provides a consistent framework for estimating savings and savings uncertainty in retrofit projects. We review the mathematical background of Bayesian inference and Bayesian regression, and present two examples of estimating savings and savings uncertainty in retrofit projects. The first is a simple case where both baseline and post-retrofit monthly natural gas use can be modeled as a linear function of monthly heating degree days. The Efficiency Valuation Organization (EVO 2007) defines two methods of determining savings in such cases: reporting period savings, which is an estimate of the savings during the post-retrofit period; and normalized savings, which is an estimate of the savings that would be obtained during a typical year at the project site. For reporting period savings, classical statistical analysis provides exact analytic results for both savings and savings uncertainty in this case. We use Bayesian analysis to calculate reporting period savings and savings uncertainty and show that the results are identical to the analytical results. For normalized savings, the literature contains no exact expression for the uncertainty of normalized savings; we use Bayesian inference to calculate this quantity for the first time, and compare it with the result of an approximate formula that has been proposed. The second example concerns a problem where the baseline data exhibit nonlinearity and serial autocorrelation, both of which are common in real-world retrofit projects. No analytical solutions exist to determine savings or savings uncertainty in this situation, but several simplified formulas have been proposed. We model the data using a 5-parameter model with first-order autoregressive errors, and use Bayesian inference to develop distributions for the model parameters and for the reporting period savings, which allows us to determine the savings uncertainty. We find the energy savings to be about 5% lower than the result obtained by ignoring the autocorrelation. In addition, the Bayesian analysis finds the savings uncertainty to be narrower than the approximate uncertainty calculated using the simplified formula. These results show that Bayesian inference can be used to determine savings and savings uncertainty for a wide variety of real-world problems.

Im, Piljae↗
DOE OSTI2011

VERY WIDE BINARIES AND OTHER COMOVING STELLAR COMPANIONS: A BAYESIAN ANALYSIS OF THE HIPPARCOS CATALOGUE

We develop Bayesian statistical methods for discovering and assigning probabilities to non-random (e.g., physical) stellar companions. These companions are either presently bound or were previously bound. The probabilities depend on similarities in corrected proper motion parallel and perpendicular to the brighter component's motion, parallax, and the local phase-space density of field stars. Control experiments are conducted to understand the behavior of false positives. The technique is applied to the Hipparcos Catalogue within 100 pc. This is the first all-sky survey to locate escaped companions still drifting along with each other. In the <100 pc distance range, {approx}220 high probability companions with separations between 0.01 and 1 pc are found. The first evidence for a population ({approx}300) of companions separated by 1-8 pc is found. We find these previously unnoticed naked-eye companions (both with V < 6th mag): Capella and 50 Per, {delta} Vel and HIP 43797, Alioth ({epsilon} UMa), Megrez ({delta} UMa) and Alcor, {gamma} and {tau} Cen, {phi} Eri and {eta} Hor, 62 and 63 Cnc, {gamma} and {tau} Per, {zeta} and {delta} Hya, {beta}{sup 01}, {beta}{sup 02} and {beta}{sup 03} Tuc, N Vel and HIP 47479, HIP 98174 and HIP 97646, and s Eri and HIP 14913. High probability fainter companions (>6th mag) of primaries with V < 4 are found for: Fomalhaut ({alpha} PsA), {gamma} UMa, {alpha} Lib, Alvahet ({iota} Cephi), {delta} Ara, {beta} Ser, {iota} Peg, {beta} Pic, {kappa} Phe, and {gamma} Tuc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Bayesian analysis of energy and count rate data for detection of low count rate radioactive sources

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a 'machine learning' approach, in which the algorithm updates and improves its characterization of background over time. The system would have a 'learning mode,' in which it measures and analyzes background count rates, and a 'detection mode,' in which it compares measurements from an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a nonparametric sampling distribution. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Bayesian analysis of the flutter margin method in aeroelasticity

A Bayesian statistical framework is presented for Zimmerman and Weissenburger flutter margin method which considers the uncertainties in aeroelastic modal parameters. The proposed methodology overcomes the limitations of the previously developed least-square based estimation technique which relies on the Gaussian approximation of the flutter margin probability density function (pdf). Using the measured free-decay responses at subcritical (preflutter) airspeeds, the joint non-Gaussain posterior pdf of the modal parameters is sampled using the Metropolis–Hastings (MH) Markov chain Monte Carlo (MCMC) algorithm. The posterior MCMC samples of the modal parameters are then used to obtain the flutter margin pdfs and finally the flutter speed pdf. The usefulness of the Bayesian flutter margin method is demonstrated using synthetic data generated from a two-degree-of-freedom pitch-plunge aeroelastic model. The robustness of the statistical framework is demonstrated using different sets of measurement data. In conclusion, it will be shown that the probabilistic (Bayesian) approach reduces the number of test points required in providing a flutter speed estimate for a given accuracy and precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Extending the predictive power of perturbative QCD using the principle of maximum conformality and the Bayesian analysis

In addition to the evaluation of high-order loop contributions, the precision and predictive power of perturbative QCD (pQCD) predictions depends on two important issues: (1) how to achieve a reliable, convergent fixed-order series, and (2) how to reliably estimate the contributions of unknown higher-order terms. The recursive use of renormalization group equation, together with the Principle of Maximum Conformality (PMC), eliminates the renormalization scheme-and-scale ambiguities of the conventional pQCD series. The result is a conformal, scale-invariant series of finite order which also satisfies all of the principles of the renormalization group. In this paper we propose a novel Bayesian-based approach to estimate the size of the unknown higher order contributions based on an optimized analysis of probability distributions. We show that by using the PMC conformal series, in combination with the Bayesian analysis, one can consistently achieve high degree of reliability estimates for the unknown high order terms. Thus the predictive power of pQCD can be greatly improved. We illustrate this procedure for two pQCD observables: R e+e– and R τ , which are each known up to four loops in pQCD. Numerical analyses confirm that by using the scale-independent and more convergent PMC conformal series, one can achieve reliable Bayesian probability estimates for the unknown higher-order contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bayesian analysis for estimating statistical parameter distributions of elasto-viscoplastic material models

High temperature design methods rely on constitutive models for inelastic deformation and failure typically calibrated against the mean of experimental data without considering the associated scatter. Variability may arise from the experimental data acquisition process, from heat-to-heat material property variations, or both and need to be accurately captured to predict parameter bounds leading to efficient component design. Applying the Bayesian Markov Chain Monte Carlo (MCMC) method to produce statistical models capturing the underlying uncertainty in the experimental data is an area of ongoing research interest. This work varies aspects of the Bayesian MCMC method and explores their effect on the posterior parameter distributions for a uniaxial elasto-viscoplastic damage model using synthetically generated reference data. From our analysis with the uniaxial inelastic model we determine that an informed prior distribution including different types of test conditions results in more accurate posterior parameter distributions. The parameter posterior distributions, however, do not improve when increasing the number of similar experimental data. Additionally, changing the amount of scatter in the data affects the quality of the posterior distributions, especially for the less sensitive model parameters. Moreover, we perform a sensitivity study of the model parameters against the likelihood function prior to the Bayesian analysis. The results of the sensitivity analysis help to determine the reliability of the posterior distributions and reduce the dimensionality of the problem by fixing the insensitive parameters. The comprehensive study described in this work demonstrates how to efficiently apply the Bayesian MCMC methodology to capture parameter uncertainties in high temperature inelastic material models. Quantifying these uncertainties in inelastic models will improve high temperature engineering design practices and lead to safer, more effective component designs.

42 ENGINEERING↗
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 OSTI2019

Joint Bayesian analysis of large angular scale CMB temperature anomalies

Cosmic microwave background measurements show an agreement with the concordance cosmology model except for a few notable anomalies: Power Suppression , the lack of large scale power in the temperature data compared to what is expected in the concordance model, and Cosmic Hemispherical Asymmetry , a dipolar breakdown of statistical isotropy. An expansion of the CMB covariance in Bipolar Spherical Harmonics naturally parametrizes both these large-scale anomalies, allowing us to perform an exhaustive, fully Bayesian joint analysis of the power spectrum and violations of statistical isotropy up to the dipole level. Our analysis sheds light on the scale dependence of the Cosmic Hemispherical Asymmetry. Assuming a scale-dependent dipole modulation model with a two-parameter power law form, we explore the posterior pdf of amplitude A($l$ = 16) and the power law index α and find the maximum a posteriori values A * (l = 16) = 0.064 ± 0.022 and $α_*$ = -0.92 ± 0.22. The maximum a posteriori direction associated with the Cosmic Hemispherical Asymmetry is ($l,b$) = (247.8 o , -19.6 o ) in Galactic coordinates, consistent with previous analyses. We evaluate the Bayes factor BSI-DM to compare the Cosmic Hemispherical Asymmetry model with the isotropic model. The data prefer but do not substantially favor the anisotropic model (BSI-DM=0.4). We consider several priors and find that this evidence ratio is robust to prior choice. The large-scale power suppression does not soften when jointly inferring both the isotropic power spectrum and the parameters of the asymmetric model, indicating no evidence that these anomalies are coupled.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

SDSS-IV MaNGA: Bayesian analysis of the star formation history of low-mass galaxies in the local Universe

ABSTRACT We measure the star formation histories (SFHs) of a sample of low-mass galaxies with M* < 109 M⊙ from the SDSS-IV MaNGA survey. The large number of IFU spectra for each galaxy are either combined to reach a high signal to noise ratio or used to investigate spatial variations. We use Bayesian inferences based on full spectrum fitting. Our analysis based on Bayesian evidence ratio indicates a strong preference for a model that allows the presence of an old stellar population, and that an improper model for the SFH can significantly underestimate the old population in these galaxies. The addition of near-infrared photometry to the constraining data can further distinguish between different SFH model families and significantly tighten the constraints on the mass fraction in the old population. On average more than half of the stellar mass in present-day low-mass galaxies formed at least 8 Gyr ago, while about 30 per cent within the past 4 Gyr. Satellite galaxies on average have formed their stellar mass earlier than central galaxies. The radial dependence of the SFH is quite weak. Our results suggest that most of the low-mass galaxies have an early episode of active star formation that produces a large fraction of their present stellar mass.

Zhou, Shuang↗
DOE OSTI2013

Using Bayesian analysis and Gaussian processes to infer electron temperature and density profiles on the Mega-Ampere Spherical Tokamak experiment

A unified, Bayesian inference of midplane electron temperature and density profiles using both Thomson scattering (TS) and interferometric data is presented. Beyond the Bayesian nature of the analysis, novel features of the inference are the use of a Gaussian process prior to infer a mollification length-scale of inferred profiles and the use of Gauss-Laguerre quadratures to directly calculate the depolarisation term associated with the TS forward model. Results are presented from an application of the method to data from the high resolution TS system on the Mega-Ampere Spherical Tokamak, along with a comparison to profiles coming from the standard analysis carried out on that system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bayesian analysis of nucleon-nucleon scattering data in pionless effective field theory

We perform Bayesian model calibration of two-nucleon (NN) low-energy constants (LECs) appearing in an NN interaction based on pionless effective field theory (πEFT). The calibration is carried out for potentials constructed using naive dimensional analysis in NN relative momenta (p) up to next-to-leading order [NLO, O(p 2 )] and next-to-next-to-next-to-leading order [N3LO, O(p 4 )]. We consider two classes of pionless πEFT potential: one that acts in all partial waves and another that is dominated by s-wave physics. The two classes produce broadly similar results for calibrations to NN data up to E lab = 5 MeV. Our analysis accounts for the correlated uncertainties that arise from the truncation of the pionless πEFT. We simultaneously estimate both the πEFT LECs and the parameters that quantify the truncation error. This permits the first quantitative estimates of the pionless πEFT breakdown scale, Λ b : the 95% intervals are Λ b ∈[50.11,63.03] MeV at NLO and Λ b ∈[72.27,88.54] MeV at N3LO. Furthermore, invoking naive dimensional analysis for the NN potential, therefore, does not lead to consistent results across orders in pionless πEFT. This exemplifies the possible use of Bayesian tools to identify inconsistencies in a proposed EFT power counting.

Bayesian methods↗
DOE OSTI2015

Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey

Recent application of the Bayesian algorithm \textsc(borg) to the Sloan Digital Sky Survey (SDSS) main sample galaxies resulted in the physical inference of the formation history of the observed large-scale structure from its origin to the present epoch. In this work, we use these inferences as inputs for a detailed probabilistic cosmic web-type analysis. To do so, we generate a large set of data-constrained realizations of the large-scale structure using a fast, fully non-linear gravitational model. We then perform a dynamic classification of the cosmic web into four distinct components (voids, sheets, filaments, and clusters) on the basis of the tidal field. Our inference framework automatically and self-consistently propagates typical observational uncertainties to web-type classification. As a result, this study produces accurate cosmographic classification of large-scale structure elements in the SDSS volume. By also providing the history of these structure maps, the approach allows an analysis of the origin and growth of the early traces of the cosmic web present in the initial density field and of the evolution of global quantities such as the volume and mass filling fractions of different structures. For the problem of web-type classification, the results described in this work constitute the first connection between theory and observations at non-linear scales including a physical model of structure formation and the demonstrated capability of uncertainty quantification. A connection between cosmology and information theory using real data also naturally emerges from our probabilistic approach. Our results constitute quantitative chrono-cosmography of the complex web-like patterns underlying the observed galaxy distribution.

79 ASTRONOMY AND ASTROPHYSICS↗