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Results for “Bayesian sensitivity analysis”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Revisiting Bayesian constraints on the transport coefficients of QCD

Multistage models based on relativistic viscous hydrodynamics have proven successful in describing hadron measurements from relativistic nuclear collisions. These measurements are sensitive to the shear and the bulk viscosities of QCD and provide a unique opportunity to constrain these transport coefficients. Bayesian analyses can be used to obtain systematic constraints on the viscosities of QCD, through methodical model-to-data comparisons. In this manuscript, we discuss recent developments in Bayesian analyses of heavy ion collision data. Furthermore, we highlight the essential role of closure tests in validating a Bayesian analysis before comparison with measurements. We discuss the role of the emulator that is used as proxy for the multistage theoretical model. We use an ongoing Bayesian analysis of soft hadron measurements by the JETSCAPE Collaboration as context for the discussion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A Framework for Parametric and Predictive Uncertainty Quantification in the E3SM Land Model: Assessing Site and Observable Generalizability

Quantifying parametric uncertainty using observations from individual sites provides a critical foundation for Earth system modeling, serving as a necessary first step before scaling up to regional or global applications. This study introduces a novel computational framework designed to enhance model predictability by reducing parametric uncertainty and assessing site and observable generalizability using various observational constraints. The framework integrates five components: Model Simulation, Statistical Emulation, Global Sensitivity Analysis (GSA), Model Calibration, and Model Prediction. Using the E3SM land model, we simulated site-level land-atmosphere carbon and energy fluxes from 2003 to 2007 across five evergreen needleleaf FLUXNET sites, perturbing 26 vegetation-related model parameters. Gaussian process emulators were employed to expedite GSA and model calibration. Four critical parameters that strongly influence selected land-atmosphere fluxes were identified by GSA. Bayesian approaches were used to infer parameter probability distributions leveraging synthetic data and FLUXNET observations. The results reveal that posterior parameter distributions vary significantly across different sites and observables within the same plant functional type. Probabilistic predictions indicate that parameters calibrated at one site can enhance predictive accuracy at other sites, although site heterogeneity may sometimes outweigh parametric uncertainty. Additionally, the probabilistic predictions demonstrate that calibration for one variable can also improve predictability for other variables, thereby maximizing predictive capabilities with limited observations. This framework provides a powerful approach for reducing parametric uncertainty in Earth system models and deepening our understanding of carbon dynamics and energy cycles. Its adaptability makes it a valuable tool for broader applications in Earth system modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Improved Limit on Neutrinoless Double-Beta Decay in Te 130 with CUORE

We report new results from the search for neutrinoless double-beta decay in $^{130}$Te with the CUORE detector. This search benefits from a four-fold increase in exposure, lower trigger thresholds and analysis improvements relative to our previous results. We observe a background of $(1.38\pm0.07)\cdot10^{-2}$ counts$/($keV$\cdot$kg$\cdot$yr$)$ in the $0\nu\beta\beta$ decay region of interest and, with a total exposure of 372.5 kg$\cdot$yr, we attain a median exclusion sensitivity of $1.7\cdot10^{25}$ yr. We find no evidence for $0\nu\beta\beta$ decay and set a $90\%$ CI Bayesian lower limit of $3.2\cdot10^{25}$ yr on the $^{130}$Te half-life for this process. In the hypothesis that $0\nu\beta\beta$ decay is mediated by light Majorana neutrinos, this results in an upper limit on the effective Majorana mass of 75-350 meV, depending on the nuclear matrix elements used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification

SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.

58 GEOSCIENCES↗
DOE OSTI2021

O 16 O 16 collisions at energies available at the BNL Relativistic Heavy Ion Collider and at the CERN Large Hadron Collider comparing α clustering versus substructure

Collisions of . light and heavy nuclei in relativistic heavy-ion collisions have been shown to be sensitive to nuclear structure. With a proposed 16 O 16 O run at the CERN Large Hadron Collider (LHC) and at the BNL Relativistic Heavy Ion Collider (RHIC) we study the potential for finding α clustering in 16 O. Here we use the state-of-the-art iEBE-VISHNU package with 16 O nucleonic configurations from ab initio nuclear lattice simulations. This setup was tuned using a Bayesian analysis on p Pb and PbPb systems. We find that the 16 O 16 O system always begins far from equilibrium and that at LHC and RHIC it approaches the regime of hydrodynamic applicability only at very late times. Finally, by taking ratios of flow harmonics we are able to find measurable differences between α-clustering, nucleonic, and subnucleonic degrees of freedom in the initial state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Interatomic Potentials Models for Cu-Ni and Cu-Zr Alloys

This study explores a Bayesian calibration framework for the RAMPAGE alloy potential model for Cu-Ni and Cu-Zr systems, respectively. In RAMPAGE potentials, it is proposed that once calibrated potentials for individual elements are available, the inter-species interactions can be described by fitting a Morse potential for pair interactions with three parameters, while densities for the embedding function can be scaled by two parameters from the elemental densities. Global sensitivity analysis tools were employed to understand the impact each parameter has on the MD simulation results. A transitional Markov Chain Monte Carlo algorithm was used to generate samples from the multimodal posterior distribution consistent with the discrepancy between MD simulation results and DFT data. For the Cu-Ni system the posterior predictive tests indicate that the fitted interatomic potential model agrees well with the DFT data, justifying the basic RAMPAGE assumptions. For the Cu-Zr system, where the phase diagram suggests more complicated atomic interactions than in the case of Cu-Ni, the RAMPAGE potential captured only a subset of the DFT data. The resulting posterior distribution for the 5 model parameters exhibited several modes, with each mode corresponding to specific simulation data and a suboptimal agreement with the DFT results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Current and future constraints on Higgs couplings in the nonlinear Effective Theory

Here, we perform a Bayesian statistical analysis of the constraints on the nonlinear Effective Theory given by the Higgs electroweak chiral Lagrangian. We obtain bounds on the effective coefficients entering in Higgs observables at the leading order, using all available Higgs-boson signal strengths from the LHC runs 1 and 2. Using a prior dependence study of the solutions, we discuss the results within the context of natural-sized Wilson coefficients. We further study the expected sensitivities to the different Wilson coefficients at various possible future colliders. Finally, we interpret our results in terms of some minimal composite Higgs models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Measurement of the electron antineutrino mass in tritium beta decay in the Troitsk nu-mass experiment

The results obtained in the Troitsk nu-mass experiment by measuring the electron-antineutrino mass in tritium beta decay are presented. The facility used consists of a gaseous windowless tritium source and an electrostatic electron spectrometer involving an adiabatic magnetic collimation. Runs in which measurement conditions were reliably established were thoroughly selected in analyzing data obtained from 1994 to 2004. All known systematic effects were taken into account. For the square of the electron-antineutrino mass, the treatment of measured spectra yielded the following result: m{sub {nu}}{sup 2} = -0.67 {+-} 1.89{sub stat.} {+-} 1.68{sub syst.} eV{sup 2}. The use of the Bayesian method and the Feldman-Cousins unified approach made it possible to obtain the following upper limits on the mass: m{sub {nu}} < 2.12 eV (at a 95% C.L.; Bayesian method) and m{sub {nu}} < 2.05 eV (at a 95% C.L., Feldman-Cousins method). At the same time, an estimation of the sensitivity limit without allowance for negative values of the square of the mass leads to m{sub {nu}} < 2.2 eV (at a 95% C.L.). Measured spectra were analyzed for the possible existence of an additional structure (step) in the electron spectrum near the boundary energy. The conclusion drawn from this analysis was that, within the existing statistical errors, there are no reasons for introducing such a feature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

MOA-2011-BLG-262Lb: A sub-Earth-mass moon orbiting a gas giant primary or a high velocity planetary system in the galactic Bulge

We present the first microlensing candidate for a free-floating exoplanet-exomoon system, MOA-2011-BLG-262, with a primary lens mass of M {sub host} ∼ 4 Jupiter masses hosting a sub-Earth mass moon. The argument for an exomoon hinges on the system being relatively close to the Sun. The data constrain the product M{sub L} π{sub rel} where M{sub L} is the lens system mass and π{sub rel} is the lens-source relative parallax. If the lens system is nearby (large π{sub rel}), then M{sub L} is small (a few Jupiter masses) and the companion is a sub-Earth-mass exomoon. The best-fit solution has a large lens-source relative proper motion, μ{sub rel} = 19.6 ± 1.6 mas yr{sup –1}, which would rule out a distant lens system unless the source star has an unusually high proper motion. However, data from the OGLE collaboration nearly rule out a high source proper motion, so the exoplanet+exomoon model is the favored interpretation for the best fit model. However, there is an alternate solution that has a lower proper motion and fits the data almost as well. This solution is compatible with a distant (so stellar) host. A Bayesian analysis does not favor the exoplanet+exomoon interpretation, so Occam's razor favors a lens system in the bulge with host and companion masses of M{sub host}=0.12{sub −0.06}{sup +0.19} M{sub ⊙} and m{sub comp}=18{sub −10}{sup +28} M{sub ⊕}, at a projected separation of a{sub ⊥}=0.84{sub −0.14}{sup +0.25} AU. The existence of this degeneracy is an unlucky accident, so current microlensing experiments are in principle sensitive to exomoons. In some circumstances, it will be possible to definitively establish the mass of such lens systems through the microlensing parallax effect. Future experiments will be sensitive to less extreme exomoons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Global Fits of the Minimal Universal Extra Dimensions Scenario

In theories with Universal Extra-Dimensions (UED), the {gamma}{sub 1} particle, first excited state of the hypercharge gauge boson, provides an excellent Dark Matter (DM) candidate. Here we use a modified version of the SuperBayeS code to perform a Bayesian analysis of the minimal UED scenario, in order to assess its detectability at accelerators and with DM experiments. We derive in particular the most probable range of mass and scattering cross sections off nucleons, keeping into account cosmological and electroweak precision constraints. The consequences for the detectability of the {gamma}{sub 1} with direct and indirect experiments are dramatic. The spin-independent cross section probability distribution peaks at {approx} 10{sup -11} pb, i.e. below the sensitivity of ton-scale experiments. The spin-dependent cross-section drives the predicted neutrino flux from the center of the Sun below the reach of present and upcoming experiments. The only strategy that remains open appears to be direct detection with ton-scale experiments sensitive to spin-dependent cross-sections. On the other hand, the LHC with 1 fb{sup -1} of data should be able to probe the current best-fit UED parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2010

Uncertainty quantification given discontinuous climate model response and a limited number of model runs.

Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of discontinuous model data with adjustable sharpness and structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Which Combinations of Environmental Conditions and Microphysical Parameter Values Produce a Given Orographic Precipitation Distribution?

This study applies an idealized modeling framework, alongside a Bayesian Markov chain Monte Carlo (MCMC) algorithm, to explore which combinations of upstream environmental conditions and cloud microphysical parameter values can produce a particular precipitation distribution over an idealized two-dimensional, bell-shaped mountain. Simulations focus on orographic precipitation produced when an atmospheric river interacts with topography. MCMC-based analysis reveals that different combinations of parameter values produce a similar precipitation distribution, with the most influential parameters being relative humidity (RH), horizontal wind speed (U), surface potential temperature (θ sfc ), and the snow fall speed coefficient (A s ). RH, U, and A s exhibit interdependence: changes in one or more of these factors can be mitigated by compensating changes in the other(s) to produce similar orographic precipitation rates. The results also indicate that the parameter sensitivities and relationships can vary for spatial subregions and given different environmental conditions. In particular, high θ sfc values are more likely to produce the target precipitation rate and spatial distribution, and thus the ensemble of simulations shows a preference for liquid precipitation at the surface. The results presented here highlight the complexity of orographic precipitation controls, and have implications for flood and water management, observational efforts, and climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

How to estimate soil organic carbon stocks of agricultural fields? perspectives using ex-ante evaluation

Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. Here, we recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using Bayesian Methodology to Estimate Liquefied Natural Gas Leak Frequencies

This analysis provides estimates on the leak frequencies of nine components found in liquefied natural gas (LNG) facilities. Data was taken from a variety of sources, with 25 different data sets included in the analysis. A hierarchical Bayesian model was used that assumes that the log leak frequency follows a normal distribution and the logarithm of the mean of this normal distribution is a linear function of the logarithm of the fractional leak area. This type of model uses uninformed prior distributions that are updated with applicable data. Separate models are fit for each component listed. Five order-of-magnitude fractional leak areas are considered, based on the flow area of the component. Three types of supporting analyses were performed: sensitivity of the model to the data set used, sensitivity of the leak frequency estimates to differences in the model structure or prior distributions, and sufficiency of sample sized used for convergence. Recommended leak frequency distributions for all component types and leak sizes are given. These leak frequency predictions can be used for quantitative risk assessments in the future.

03 NATURAL GAS↗
NASA NTRS2003

Analysis Methods and Results for Weak Gamma-Ray Bursts in the BATSE Data

We report initial results on the statistical properties of the dimmest gamma-ray bursts (GRBs) observed with the Burst and Transient Source Experiment (BATSE), using new ground-based methods to obtain a sample of GRBs from 502 days of BATSE data. Using the most sensitive ground-based detection of GRBs, the sample extends to GRBs much fainter than those detected by the on-board trigger, but because of the temporal resolution of the data, the sample is limited to GRBs of duration of at least 2(approx.)s. For each detected event, Bayesian probabilities are calculated for the event to belong to each of seven classes of differing physical origins. The sample of GRB candidates is defined by the requirement that the Bayesian probability for belonging to the GRB class is higher than 0.5. The intensity distribution of the GRB sample is corrected using a Monte Carlo simulation of the post-flight detection efficiency. The dimmest BATSE bursts of the sample continue the hardness-intensity trend seen in brighter GREs and are consistent with isotropy.

Mitrofanov, I. G.↗
DOE OSTI2011

Measuring $\theta_{13}$ via Muon Neutrino to Electron Neutrino Oscillations in the MINOS Experiment

One of the primary goals in neutrino physics at the present moment is to make a measurement of the neutrino oscillation parameter $\theta_{13}$. This parameter, in addition to being unknown, could potentially allow for the introduction of CP violation into the lepton sector. The MINOS long-baseline neutrino oscillation experiment has the ability to make a measurement of this parameter, by looking for the oscillation of muon neutrinos to electron neutrinos between a Near and Far Detector over a distance of 735 km. This thesis discusses the development of an analysis framework to search for this oscillation mode. Two major improvements to pre-existing analysis techniques have been implemented by the author. First, a novel particle ID technique based on strip topology, known as the Library Event Matching (LEM) method, is optimized for use in MINOS. Second, a multiple bin likelihood method is developed to fit the data. These two improvements, when combined, increase MINOS' sensitivity to $\sin^2(2\theta_{13})$ by 27\% over previous analyses. This thesis sees a small excess over background in the Far Detector. A Frequentist interpretation of the data rules out $\theta_{13}=0$ at 91\%. A Bayesian interpretation of the data is also presented, placing the most stringent upper boundary on the oscillation parameter to date, at $\sin^2(2\theta_{13})<0.09(0.015)$ for the Normal (Inverted) Hierarchy and $\delta_{CP}=0$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗