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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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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2021

Bayesian analysis of a future β decay experiment's sensitivity to neutrino mass scale and ordering

Bayesian modeling techniques enable sensitivity analyses that incorporate detailed expectations regarding future experiments. A model-based approach also allows one to evaluate inferences and predicted outcomes, by calibrating (or measuring) the consequences incurred when certain results are reported. In this work, we present procedures for calibrating predictions of an experiment's sensitivity to both continuous and discrete parameters. Using these procedures and a new Bayesian model of the β-decay spectrum, we assess a high-precision β-decay experiment's sensitivity to the neutrino mass scale and ordering for one assumed design scenario. We find that such an experiment could measure the electron-weighted neutrino mass within ~40 meV after 1 year (90% credibility). Neutrino masses > 500 meV could be measured within ≈5 meV. Using only β decay and external reactor neutrino data, we find that next-generation β-decay experiments could potentially constrain the mass ordering using a two-neutrino spectral model analysis. By calibrating mass ordering results, we identify reporting criteria that can be tuned to suppress false ordering claims. In some cases, a two-neutrino analysis can reveal that the mass ordering is inverted, an unobtainable result for the traditional one-neutrino analysis approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

BEYONDPLANCK IV. On end-to-end simulations in CMB analysis — Bayesian versus frequentist statistics

End-to-end simulations play a key role in the analysis of any high-sensitivity cosmic microwave background (CMB) experiment, providing high-fidelity systematic error propagation capabilities that are unmatched by any other means. In this paper, we address an important issue regarding such simulations, namely, how to define the inputs in terms of sky model and instrument parameters. These may either be taken as a constrained realization derived from the data or as a random realization independent from the data. We refer to these as posterior and prior simulations, respectively. We show that the two options lead to significantly different correlation structures, as prior simulations (contrary to posterior simulations) effectively include cosmic variance, but they exclude realization-specific correlations from non-linear degeneracies. Consequently, they quantify fundamentally different types of uncertainties. We argue that as a result, they also have different and complementary scientific uses, even if this dichotomy is not absolute. In particular, posterior simulations are in general more convenient for parameter estimation studies, while prior simulations are generally more convenient for model testing. Before BEYONDPLANCK, most pipelines used a mix of constrained and random inputs and applied the same hybrid simulations for all applications, even though the statistical justification for this is not always evident. BEYONDPLANCK represents the first end-to-end CMB simulation framework that is able to generate both types of simulations and these new capabilities have brought this topic to the forefront. The BEYONDPLANCK posterior simulations and their uses are described extensively in a suite of companion papers. In this work, we consider one important applications of the corresponding prior simulations, namely, code validation. Specifically, we generated a set of one-year LFI 30 GHz prior simulations with known inputs and we used these to validate the core low-level BEYONDPLANCK algorithms dealing with gain estimation, correlated noise estimation, and mapmaking.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Bayesian Analysis of Step-Stress Accelerated Life Test with Exponential Distribution

In this article, we propose a general Bayesian inference approach to the step-stress accelerated life test with type II censoring. We assume that the failure times at each stress level are exponentially distributed and the test units are tested in an increasing order of stress levels. We formulate the prior distribution of the parameters of life-stress function and integrate the engineering knowledge of product failure rate and acceleration factor into the prior. The posterior distribution and the point estimates for the parameters of interest are provided. Through the Markov chain Monte Carlo technique, we demonstrate a nonconjugate prior case using an industrial example. It is shown that with the Bayesian approach, the statistical precision of parameter estimation is improved and, consequently, the required number of failures could be reduced.

14 SOLAR ENERGY↗
DOE OSTI2014

Bayesian analysis for kaon photoproduction

We have investigated contribution of the nucleon resonances in the kaon photoproduction process by using an established statistical decision making method, i.e. the Bayesian method. This method does not only evaluate the model over its entire parameter space, but also takes the prior information and experimental data into account. The result indicates that certain resonances have larger probabilities to contribute to the process.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Bayesian analysis of cosmic ray propagation: evidence against homogeneous diffusion

We present the results of the most complete scan of the parameter space for cosmic ray (CR) injection and propagation. We perform a Bayesian search of the main GALPROP parameters, using the MultiNest nested sampling algorithm, augmented by the BAMBI neural network machine-learning package. This is the first study to separate out low-mass isotopes (p, p-bar , and He) from the usual light elements (Be, B, C, N, and O). We find that the propagation parameters that best-fit p, p-bar , and He data are significantly different from those that fit light elements, including the B/C and {sup 10}Be/{sup 9}Be secondary-to-primary ratios normally used to calibrate propagation parameters. This suggests that each set of species is probing a very different interstellar medium, and that the standard approach of calibrating propagation parameters using B/C can lead to incorrect results. We present posterior distributions and best-fit parameters for propagation of both sets of nuclei, as well as for the injection abundances of elements from H to Si. The input GALDEF files with these new parameters will be included in an upcoming public GALPROP update.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Bayesian analysis of CCDM models

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

MAGNETIC FIELDS IN INTERSTELLAR CLOUDS FROM ZEEMAN OBSERVATIONS: INFERENCE OF TOTAL FIELD STRENGTHS BY BAYESIAN ANALYSIS

The only direct measurements of interstellar magnetic field strengths depend on the Zeeman effect, which samples the line-of-sight component B{sub z} of the magnetic vector. In this paper, we use a Bayesian approach to analyze the observed probability density function (PDF) of B{sub z} from Zeeman surveys of H I, OH, and CN spectral lines in order to infer a density-dependent stochastic model of the total field strength B in diffuse and molecular clouds. We find that at n < 300 cm{sup -3} (in the diffuse interstellar medium sampled by H I lines), B does not scale with density. This suggests that diffuse clouds are assembled by flows along magnetic field lines, which would increase the density but not the magnetic field strength. We further find strong evidence for B in molecular clouds being randomly distributed between very small values and a maximum that scales with volume density n as B {proportional_to} n {sup 0.65} for n>300 cm{sup -3}, with an uncertainty at the 50% level in the power-law exponent of about {+-}0.05. This break-point density could be interpreted as the average density at which parsec-scale clouds become self-gravitating. Both the uniform PDF of total field strengths and the scaling with density suggest that magnetic fields in molecular clouds are often too weak to dominate the star formation process. The stochasticity of the total field strength B implies that many fields are so weak that the mass/flux ratio in many clouds must be significantly supercritical. A two-thirds power law comes from isotropic contraction of gas too weakly magnetized for the magnetic field to affect the morphology of the collapse. On the other hand, our study does not rule out some clouds having strong magnetic fields with critical mass/flux ratios.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Bayesian Analysis of the Cosmic Microwave Background

There is a wealth of cosmological information encoded in the spatial power spectrum of temperature anisotropies of the cosmic microwave background! Experiments designed to map the microwave sky are returning a flood of data (time streams of instrument response as a beam is swept over the sky) at several different frequencies (from 30 to 900 GHz), all with different resolutions and noise properties. The resulting analysis challenge is to estimate, and quantify our uncertainty in, the spatial power spectrum of the cosmic microwave background given the complexities of "missing data", foreground emission, and complicated instrumental noise. Bayesian formulation of this problem allows consistent treatment of many complexities including complicated instrumental noise and foregrounds, and can be numerically implemented with Gibbs sampling. Gibbs sampling has now been validated as an efficient, statistically exact, and practically useful method for low-resolution (as demonstrated on WMAP 1 and 3 year temperature and polarization data). Continuing development for Planck - the goal is to exploit the unique capabilities of Gibbs sampling to directly propagate uncertainties in both foreground and instrument models to total uncertainty in cosmological parameters.

methods - statistical↗