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At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable atmospheric conditions are evaluated, with a focus on gases for which fast sensors are not readily available. First, the relaxed eddy accumulation (REA) classical approach and a recently proposed mixing length parameterization, labeled A22, are tested against eddy-covariance computations. Using high-frequency measurements collected from two contrasting sites (the frozen tundra near Utqiaġvik, Alaska, and a sparsely vegetated grassland in Wendell, Idaho, during winter), it is shown that the REA and A22 models outperform the conventional Monin–Obukhov similarity theory (MOST) utilized widely to infer fluxes from mean gradients. Second, scenarios where slow trace gas sensors are the only viable option in field measurements are investigated using digital filtering applied to fast-response sensors to simulate their slow-response counterparts. With a filtered scalar signal, the observed filtered eddy-covariance fluxes are referred to here as large-eddy-covariance (LEC) fluxes. A virtual eddy accumulation (VEA) approach, akin to the REA model but not requiring a mechanical apparatus to separate the gas flows, is also formulated and tested. A22 outperforms VEA and LEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes; however, VEA can still capture the LEC fluxes well. This finding motivates the introduction of a sensor response time correction into the VEA formulation to offset the effect of sensor filtering on the underestimated net averaged fluxes. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. The VEA approach is very suitable and simple to use with gas sensors of intermediate speed (∼ 0.5 to 1 Hz) and with conventional open- or closed-path setups.

58 GEOSCIENCES↗
DOE OSTI2016

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Two prime bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. However, incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting—both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We introduce and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

AN EMPIRICAL CALIBRATION TO ESTIMATE COOL DWARF FUNDAMENTAL PARAMETERS FROM H-BAND SPECTRA

Interferometric radius measurements provide a direct probe of the fundamental parameters of M dwarfs. However, interferometry is within reach for only a limited sample of nearby, bright stars. We use interferometrically measured radii, bolometric luminosities, and effective temperatures to develop new empirical calibrations based on low-resolution, near-infrared spectra. We find that H-band Mg and Al spectral features are good tracers of stellar properties, and derive functions that relate effective temperature, radius, and log luminosity to these features. The standard deviations in the residuals of our best fits are, respectively, 73 K, 0.027 R {sub ☉}, and 0.049 dex (an 11% error on luminosity). Our calibrations are valid from mid K to mid M dwarf stars, roughly corresponding to temperatures between 3100 and 4800 K. We apply our H-band relationships to M dwarfs targeted by the MEarth transiting planet survey and to the cool Kepler Objects of Interest (KOIs). We present spectral measurements and estimated stellar parameters for these stars. Parallaxes are also available for many of the MEarth targets, allowing us to independently validate our calibrations by demonstrating a clear relationship between our inferred parameters and the stars' absolute K magnitudes. We identify objects with magnitudes that are too bright for their inferred luminosities as candidate multiple systems. We also use our estimated luminosities to address the applicability of near-infrared metallicity calibrations to mid and late M dwarfs. The temperatures we infer for the KOIs agree remarkably well with those from the literature; however, our stellar radii are systematically larger than those presented in previous works that derive radii from model isochrones. This results in a mean planet radius that is 15% larger than one would infer using the stellar properties from recent catalogs. Our results confirm the derived parameters from previous in-depth studies of KOIs 961 (Kepler-42), 254 (Kepler-45), and 571 (Kepler-186), the latter of which hosts a rocky planet orbiting in its star's habitable zone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess

The nature of the Fermi γ-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade. Although the excess is broadly compatible with emission expected due to dark matter annihilation, an explanation in terms of a population of unresolved astrophysical point sources e.g., millisecond pulsars, remains viable. The effort to uncover the origin of the GCE is hampered in particular by an incomplete understanding of diffuse emission of Galactic origin. This can lead to spurious features that make it difficult to robustly differentiate smooth emission, as expected for a dark matter origin, from more “clumpy” emission expected from a population of relatively bright, unresolved point sources. Here, we use recent advancements in the field of simulation-based inference, in particular density estimation techniques using normalizing flows, in order to characterize the contribution of modeled components, including unresolved point source populations, to the GCE. Compared to traditional techniques based on the statistical distribution of photon counts, our machine learning-based method is able to utilize more of the information contained in a given model of the Galactic Center emission, and in particular can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the γ-ray map. This makes the method demonstrably more resilient to certain forms of model misspecification. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point sources when compared to traditional approaches. We nevertheless infer such a contribution to make up a non-negligible fraction of the GCE across all analysis variations considered, with at least $38^{+9}_{-19}$% of the excess attributed to unresolved point sources in our baseline analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

β − decay of exotic P and S isotopes with neutron number near 28

β - decay of very neutron-rich isotopes of P and S, studied at the National Superconducting Cyclotron Laboratory (NSCL) using the Beta Counting Station (BCS) consisting of a Double Sided Strip Detector surrounded by clovers detectors for observing delayed γ transitions is reported here. β-decay half-lifes and delayed neutron emission probabilities were extracted for 42,43,44 P and 44,46 S by analyzing spatial and temporal correlations between implants and decay events in the Si detector with further coincidence with γ transitions. Detection of delayed γ rays allowed for the identification of negative parity 1p1h states in 42 S for the first time, also constraining the parent ( 42 P) spin/parity to 2 - or 3 - . For the most exotic isotope studied, 46 S, no strong γ transition was observed unlike lighter even-even S isotopes, thus implying the shift of Gamow Teller (GT) strength distribution to higher energies. In conclusion, comparison of experimental observations to detailed shell-model calculations using the SDPFSDG-MU interaction allowed us to infer the importance and role of First Forbidden (FF) β transitions as the neutron number approaches and then exceeds N = 28.

39 ≤ A ≤ 58↗
DOE OSTI2016

Modeling the influence of preferential flow on the spatial variability and time-dependence of mineral weathering rates

Inferences of weathering rates from laboratory and field observations suggest significant scale and time-dependence. Preferential flow induced by heterogeneity (manifest as permeability variations or discrete fractures) has been suggested as one potential mechanism causing scale/time-dependence. In this paper, we present a quantitative evaluation of the influence of preferential flow on weathering rates using reactive transport modeling. Simulations were performed in discrete fracture networks (DFNs) and correlated random permeability fields (CRPFs), and compared to simulations in homogeneous permeability fields. The simulations reveal spatial variability in the weathering rate, multidimensional distribution of reactions zones, and the formation of rough weathering interfaces and corestones due to preferential flow. In the homogeneous fields and CRPFs, the domain-averaged weathering rate is initially constant as long as the weathering front is contained within the domain, reflecting equilibrium-controlled behavior. The behavior in the CRPFs was influenced by macrodispersion, with more spread-out weathering profiles, an earlier departure from the initial constant rate and longer persistence of weathering. DFN simulations exhibited a sustained time-dependence resulting from the formation of diffusion-controlled weathering fronts in matrix blocks, which is consistent with the shrinking core mechanism. A significant decrease in the domain-averaged weathering rate is evident despite high remaining mineral volume fractions, but the decline does not follow a math formula dependence, characteristic of diffusion, due to network scale effects and advection-controlled behavior near the inflow boundary. Finally, the DFN simulations also reveal relatively constant horizontally averaged weathering rates over a significant depth range, challenging the very notion of a weathering front.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Exact reconstruction of gene regulatory networks using compressive sensing

We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness. For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented. The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Toward Improving the Generation Quality of Autoregressive Slot VAEs

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

A taste of dark matter: Flavour constraints on pseudoscalar mediators

Dark matter interacting via the exchange of a light pseudoscalar can induce observable signals in indirect detection experiments and experience large self-interactions while evading the strong bounds from direct dark matter searches. The pseudoscalar mediator will however induce flavour-changing interactions in the Standard Model, providing a promising alternative way to test these models. We investigate in detail the constraints arising from rare meson decays and fixed target experiments for different coupling structures between the pseudoscalar and Standard Model fermions. The resulting bounds are highly complementary to the information inferred from the dark matter relic density and the constraints from primordial nucleosynthesis. We discuss the implications of our findings for the dark matter self-interaction cross section and the prospects of probing dark matter coupled to a light pseudoscalar with direct or indirect detection experiments. In particular, we find that a pseudoscalar mediator can only explain the Galactic Centre excess if its mass is above that of the B mesons, and that it is impossible to obtain a sufficiently large direct detection cross section to account for the DAMA modulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Bayesian Model Calibration for Extrapolative Prediction via Gibbs Posteriors

The current standard Bayesian approach to model calibration, which assigns a Gaussian process prior to the discrepancy term, often suffers from issues of unidentifiability and computational complexity and instability. When the goal is to quantify uncertainty in physical parameters for extrapolative prediction, then there is no need to perform inference on the discrepancy term. With this in mind, we introduce Gibbs posteriors as an alternative Bayesian method for model calibration, which updates the prior with a loss function connecting the data to the parameter. The target of inference is the physical parameter value which minimizes the expected loss. We propose to tune the loss scale of the Gibbs posterior to maintain nominal frequentist coverage under assumptions of the form of model discrepancy, and present a bootstrap implementation for approximating coverage rates. Our approach is highly modular, allowing an analyst to easily encode a wide variety of such assumptions. Furthermore, we provide a principled method of combining posteriors calculated from data subsets. We apply our methods to data from an experiment measuring the material properties of tantalum.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Investigation of the hard x-ray background in backlit pinhole imagers

Hard x-rays from laser-produced hot electrons (>10 keV) in backlit pinhole imagers can give rise to a background signal that decreases signal dynamic range in radiographs. Consequently, significant uncertainties are introduced to the measured optical depth of imaged plasmas. Past experiments have demonstrated that hard x-rays are produced when hot electrons interact with the high-Z pinhole substrate used to collimate the softer He-α x-ray source. Results are presented from recent experiments performed on the OMEGA-60 laser to further study the production of hard x-rays in the pinhole substrate and how these x-rays contribute to the background signal in radiographs. Radiographic image plates measured hard x-rays from pinhole imagers with Mo, Sn, and Ta pinhole substrates. The variation in background signal between pinhole substrates provides evidence that much of this background comes from x-rays produced in the pinhole substrate itself. A Monte Carlo electron transport code was used to model x-ray production from hot electrons interacting in the pinhole substrate, as well as to model measurements of x-rays from the irradiated side of the targets, recorded by a bremsstrahlung x-ray spectrometer. Inconsistencies in inferred hot electron distributions between the different pinhole substrate materials demonstrate that additional sources of hot electrons beyond those modeled may produce hard x-rays in the pinhole substrate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

HYSTERESIS BETWEEN DISTINCT MODES OF TURBULENT DYNAMOS

Nonlinear mean-field models of the solar dynamo show long-term variability, which may be relevant to different states of activity inferred from long-term radiocarbon data. This paper is aimed at probing the dynamo hysteresis predicted by the recent mean-field models of Kitchatinov and Olemskoy with direct numerical simulations. We perform three-dimensional (3D) simulations of large-scale dynamos in a shearing box with helically forced turbulence. As an initial condition, we either take a weak random magnetic field or we start from a snapshot of an earlier simulation. Two quasi-stable states are found to coexist in a certain range of parameters close to the onset of the large-scale dynamo. The simulations converge to one of these states depending on the initial conditions. When either the fractional helicity or the magnetic Prandtl number is increased between successive runs above the critical value for onset of the dynamo, the field strength jumps to a finite value. However, when the fractional helicity or the magnetic Prandtl number is then decreased again, the field strength stays at a similar value (strong field branch) even below the original onset. We also observe intermittent decaying phases away from the strong field branch close to the point where large-scale dynamo action is just possible. The dynamo hysteresis seen previously in mean-field models is thus reproduced by 3D simulations. Its possible relation to distinct modes of solar activity such as grand minima is discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

H{sub 2}CO Distribution and Formation in the TW HYA Disk

H{sub 2}CO is one of the most readily detected organic molecules in protoplanetary disks. Yet its distribution and dominant formation pathway(s) remain largely unconstrained. To address these issues, we present ALMA observations of two H{sub 2}CO lines (3{sub 12}--2{sub 11} and 5{sub 15}--4{sub 14}) at 0.″5 (∼30 au) spatial resolution toward the disk around the nearby T Tauri star TW Hya. Emission from both lines is spatially resolved, showing a central depression, a peak at 0.″4 radius, and a radial decline at larger radii with a bump at ∼1″, near the millimeter continuum edge. We adopt a physical model for the disk and use toy models to explore the radial and vertical H{sub 2}CO abundance structure. We find that the observed emission implies the presence of at least two distinct H{sub 2}CO gas reservoirs: (1) a warm and unresolved inner component (<10 au), and (2) an outer component that extends from ∼15 au to beyond the millimeter continuum edge. The outer component is further constrained by the line ratio to arise in a more elevated disk layer at larger radii. The inferred H{sub 2}CO abundance structure agrees well with disk chemistry models, which predict efficient H{sub 2}CO gas-phase formation close to the star, and cold H{sub 2}CO grain surface formation, through H additions to condensed CO, followed by non-thermal desorption in the outer disk. The implied presence of active grain surface chemistry in the TW Hya disk is consistent with the recent detection of CH{sub 3}OH emission, and suggests that more complex organic molecules are formed in disks, as well.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Capabilities of multivariate Bayesian inference toward seismic hazard assessment

Multivariate Bayesian analysis can bring significant benefits to seismic hazard analysis: Its multivariate feature enables computing scalar and vector hazard without making any approximations; Correlations between intensity measures are implicitly modeled, permitting direct simulation of ground motion selection tools such as the conditional mean spectrum and the generalized conditioning intensity measure; and Its updating feature enables a seamless integration of new ground motion data into the hazard results. Here, we first develop a multivariate Bayesian ground motion model through the NGA-West2 database. The model functional form considers fault-type, magnitude, and distance dependencies, and also the linear and the rock intensity dependent site response. We use a hybrid Markov Chain Monte Carlo sampling to perform Bayesian inference consisting of Gibbs step and a multilevel Metropolis-Hastings step. We then perform several checks on the model and note that its performance is satisfactory. Finally, we illustrate the merits of this multivariate Bayesian analysis, which include: ground motion model updating with ground motion data recorded in the last four years not part of the NGA-West2 database; computation of scalar and vector seismic hazard using the un-updated and updated ground motion models for Los Angeles, CA; and simulation of the conditional mean spectrum under scalar and vector IM conditioning while accounting for different sources of aleatoric and epistemic uncertainties.

58 GEOSCIENCES↗