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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Bayesian inference of multi-messenger astrophysical data: Joint and coherent inference of gravitational waves and kilonovae

Multi-messenger observations of binary neutron star mergers can provide information on the neutron star’s equation of state (EOS) above the nuclear saturation density by directly constraining the mass-radius diagram. We present a Bayesian framework for joint and coherent analyses of multi-messenger binary neutron star signals. As a first application, we analyze the gravitational-wave GW170817 and the kilonova (kN) AT2017gfo data. These results are then combined with the most recent X-ray pulsar analyses of PSR J0030+0451 and PSR J0740+6620 to obtain new EOS constraints.We extend the bajes infrastructure with a joint likelihood for multiple datasets, support for various semi-analytical kN models, and numerical-relativity (NR)-informed relations for the mass ejecta, as well as a technique to include and marginalize over modeling uncertainties. The analysis of GW170817 used the TEOBResumS effective-one-body waveform template to model the gravitational-wave signal. The analysis of AT2017gfo used a baseline multicomponent spherically symmetric model for the kN light curves. Various constraints on the mass-radius diagram and neutron star properties were then obtained by resampling over a set of ten million parameterized EOSs, which was built under minimal assumptions (general relativity and causality).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Bayesian desmearing algorithm for Bonse–Hart USANS with anisotropic scattering

Ultra-small-angle neutron scattering (USANS) using Bonse–Hart optics provides micrometer-scale structural insights but suffers from severe slit-geometry smearing. While well-established for isotropic systems, quantitative desmearing of anisotropic data remains a challenge because conventional corrections break down for non-radial scattering. In this work, we address this by developing a resolution-aware Bayesian framework that explicitly incorporates anisotropy via an affine deformation to the scattering pattern, guided by the principle of parsimony. This results in orientation-resolved point-spread functions that enable a self-consistent determination of both the resolution and deformation parameters. Using Gaussian process regression with uncertainty quantification and a probabilistic correction for multiple scattering, we demonstrate the framework’s effectiveness through numerical benchmarks and experimental studies of a stretched polymer melt. Our approach enables the seamless integration of SANS and USANS data, facilitating quantitative structural analysis of deformed materials at nanometer to micrometer scales.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data assimilation in operator algebras

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. Here, in a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.

42 ENGINEERING↗
DOE OSTI2022

Bayesian characterization of uncertainties surrounding fluvial flood hazard estimates

Fluvial floods drive severe risk to riverine communities. There is strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters, and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on the Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localization

Abstract This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of sensors which can simultaneously act as scatterers and absorbers. This imager is convenient for imaging situations requiring a wide field of view. The proposed statistical forward model is then used to solve the inverse problem of estimating the location and energy of point-like sources from observed data. This inverse problem is formulated and solved in a Bayesian framework by using a Metropolis within Gibbs algorithm for the estimation of the location, and an expectation-maximization algorithm for the estimation of the energy. This approach leads to more accurate estimation when compared with the deterministic standard back-projection approach, with the additional benefit of uncertainty quantification in the low photon imaging setting.

Tarpau, Cécilia (ORCID:0000000286539490)↗
DOE OSTI2022

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. Furthermore, a neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.

Gaussian process↗
DOE OSTI2025

Characterization of uncertainties in electron-argon collision cross sections

Abstract The predictive capability of a plasma discharge model depends on accurate representations of electron-impact collision cross sections, which determine the corresponding reaction rates and electron transport properties. The values of cross sections can be known only approximately either through experiments or simulations and are thus subject to uncertainties. Quantifying the uncertainties in plasma simulations allows us to assess the reliability of simulations and to provide a basis for interpreting discrepancies between simulations and experiments. For such uncertainty quantification of plasma simulations, it is essential to quantify the uncertainties of the underlying cross sections. Although much effort has been committed to calibrate the cross section values, their uncertainties are not well investigated. We characterize uncertainties in electron-argon atom collision cross sections using a Bayesian framework. Six collision processes—elastic momentum transfer, ionization, and four excitations—are characterized with semi-empirical models, which effectively capture the features important to the macroscopic properties of the plasma. A probability model for the uncertain parameters of these semi-empirical models is developed. Specifically, a Gaussian-process likelihood model is proposed to capture discrepancies among data sets, as well as the model-form inadequacies of the semi-empirical models. Two other likelihood models are compared with the proposed Gaussian-process model, to illustrate the importance of the choice of the likelihood model. The cross section models are calibrated using the electron-beam experiments and ab-inito quantum simulations. The resulting calibrated uncertainties capture well the scattering among the data sets. The calibrated cross section models are further validated against swarm-parameter experiments and zero-dimensional Boltzmann equation simulations of widely used cross section datasets.

Chung, Seung Whan (ORCID:0000000302501549)↗
DOE OSTI2026

Cosmological constraints from a joint DESI DR1 Full-Shape and DR2 BAO

We present a cosmological analysis combining full-shape (FS) clustering measurements from the Dark Energy Spectroscopic Instrument (DESI) DR1 with baryon acoustic oscillation (BAO) measurements from DESI DR2. To achieve a robust combination that accounts for the correlation between the two data releases, we employ the ShapeFit compression method and estimate the joint covariance using EZmocks. This compressed approach inherently mitigates the prior volume effects that have previously dominated Bayesian constraints from DESI data with minimal external priors. Consequently, we obtain — for the first time within a Bayesian framework — reliable DESI-only constraints on extensions to ΛCDM using only a Big Bang Nucleosynthesis prior on the baryon density and a wide prior on the spectral index. In flat ΛCDM, we find Ω m = 0.3035 ± 0.0085, h = 0.6876 ± 0.0059, and σ 8 = 0.822 ± 0.034. For the w 0 w a CDM dynamical dark energy model, we measure w 0 = -0.49 ± 0.25 and w a = -1.52 ± 0.77, improving constraints by ∼ 30% relative to the analogous DR1 measurement and reducing the discrepancy with ΛCDM to 1.4σ when compared to BAO only analyses. We also report competitive limits on the sum of neutrino masses and spatial curvature. This work demonstrates that the ShapeFit compression provides a prior-robust and computationally efficient pathway to constrain beyond-ΛCDM physics with large-scale structure.

baryon acoustic oscillations↗
DOE OSTI2026

Simultaneous global and local clustering in multiplex networks with covariate information

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

community detection↗
DOE OSTI2021

Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure

ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.

Price, M. A.↗
DOE OSTI2022

A deep, multi-epoch Chandra HETG study of the ionized outflow from NGC 4051

Actively accreting supermassive black holes significantly impact the evolution of their host galaxies, truncating further star formation by expelling large fractions of gas with wide-angle outflows. The X-ray band is key to understanding how these black hole winds affect their environment, as the outflows have high temperatures ~10 5–8 K). We have developed a Bayesian framework for characterizing active galactic nucleus outflows with an improved ability to explore parameter space and perform robust model selection. We applied this framework to a new 700 ks and an archival 315 ks Chandra High Energy Transmission Gratings observation of the Seyfert galaxy NGC 4051. We have detected six absorbers intrinsic to NGC 4051. These wind components span velocities from 400 to 30000 km s -1 . We have determined that the most statistically significant wind component is purely collisionally ionized, which is the first detection of such an absorber. This wind has T ≈ 10 7 K and v ≈ 880 km s -1 and remains remarkably stable between the two epochs. Other slow components also remain stable across time. Fast outflow components change their properties between 2008 and 2016, suggesting either physical changes or clouds moving in and out of the line of sight. For one of the fast components, we obtain one of the tightest wind density measurements to date, log n/(cm -3 ) = 13.0$^{+0.01}_{-0.02}$, and determine that it is located at ~240 gravitational radii. The estimated total outflow power surpasses 5 percent of the bolometric luminosity (albeit with large uncertainties) making it important in the context of galaxy–black hole interactions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The intrinsic X-ray luminosity distribution of an optically selected SDSS quasar population

ABSTRACT In active galactic nuclei, the relationship between UV and X-ray luminosity is well studied (often characterised by αox) but often with heterogeneous samples. We have parametrized the intrinsic distribution of X-ray luminosity, LX, for the optically selected sample of SDSS quasars in the Stripe 82 and XXL fields across redshifts 0.5–3.5. We make use of the available XMM observations and a custom pipeline to produce Bayesian sensitivity curves that are used to derive the intrinsic X-ray distribution in a hierarchical Bayesian framework. We find that the X-ray luminosity distribution is well described by a Gaussian function in log10 LX space with a mean that is dependent on the monochromatic 2500 Å UV luminosity, L2500. We also observe some redshift dependence of the distribution. The mean of the LX distribution increases with redshift while the width decreases. This weak but significant redshift dependence leads to L2500–LX and L2500–αox relations that evolve with redshift, and we produce a redshift- and L2500-dependent αox equation. Neither black hole mass nor Eddington ratio appear to be potential drivers of the redshift evolution.

Rankine, Amy L. (ORCID:0000000220911966)↗
DOE OSTI2024

Exploring modified gravity: constraints on the μ and Σ parametrization with WMAP , ACT, and SPT

ABSTRACT The cosmic acceleration problem remains one of the most significant challenges in cosmology. One of the proposed solutions to this problem is the modification of gravity on large scales. In this paper, we explore the well-known μ–Σ parametrization scenarios and confront them with observational data, including the cosmic microwave background (CMB) radiation from the Wilkinson Microwave Anisotropy Probe (WMAP), Atacama Cosmology Telescope (ACT), and South Pole Telescope (SPT), as well as large-scale structure data from the Sloan Digital Sky Survey (SDSS; baryon acoustic oscillation + redshift-space distortion) and Pantheon supernova (SN) catalogue. We employ a Bayesian framework to constrain the model parameters and discuss the implications of our results on the viability of modified gravity theories. Our analysis reveals the strengths and limitations of the μ–Σ parametrization and provides valuable insights into the nature of gravity on cosmological scales. From the joint analysis of the ACT + WMAP + SDSS + SN, we find μ0 − 1 = 0.02 ± 0.19 and Σ0 − 1 = 0.021 ± 0.068 at 68 per cent confidence level (CL). In light of the SPT + WMAP + SDSS + SN, we find μ0 − 1 = 0.07 ± 0.18 and $\Sigma _0 -1 = -0.009^{+0.078}_{-0.11}$ at 68 per cent CL. In all the analyses carried out, we do not find any deviations from the theory of general relativity. Our results represent an observational update on the well-known μ–Σ parametrization in view of current CMB data, independent of and competitive with the constraints obtained with the Planck data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

On data set tensions and signatures of new cosmological physics

ABSTRACT Can new cosmic physics be uncovered through tensions amongst data sets? Tensions in parameter determinations amongst different types of cosmological observation, especially the ‘Hubble tension’ between probes of the expansion rate, have been invoked as possible indicators of new physics, requiring extension of the ΛCDM paradigm to resolve. Within a fully Bayesian framework, we show that the standard tension metric gives only part of the updating of model probabilities, supplying a data co-dependence term that must be combined with the Bayes factors of individual data sets. This shows that, on its own, a reduction of data set tension under an extension to ΛCDM is insufficient to demonstrate that the extended model is favoured. Any analysis that claims evidence for new physics solely on the basis of alleviating data set tensions should be considered incomplete and suspect. We describe the implications of our results for the interpretation of the Hubble tension.

Cortês, Marina (ORCID:0000000304853767)↗
DOE OSTI2024

Bayesian inference of fine features of the nuclear equation of state from future neutron star radius measurements to 0.1 km accuracy

To more precisely constrain the equation of state (EOS) of supradense neutron-rich nuclear matter, future high-precision x-ray and gravitational wave observatories are proposed to measure the radii of neutron stars (NSs) with an accuracy better than about 0.1 km. However, it remains unclear what particular aspects (other than the stiffness generally spoken of in the literature) of the EOS and to what precision they will be better constrained. In this work, within a Bayesian framework using a metamodel EOS for NSs, we infer the posterior probability distribution functions (PDFs) of incompressibility K 0 and skewness J 0 of symmetric nuclear matter (SNM) as well as the slope L, curvature K sym , and skewness J sym characterizing the density dependence of nuclear symmetry energy E sym ⁡(ρ), respectively, from mean values of NS radii consistent with existing observations and an expected accuracy Δ⁢R ranging from about 1.0 to 0.1 km. Here, we found that (1) the Δ⁢R has little effect on inferring the stiffness of SNM at suprasaturation densities, (2) smaller Δ⁢R reveals more accurately not only the PDFs but also pairwise correlations among parameters characterizing high-density E sym ⁡(ρ), (3) a double-peak feature of the PDF(K sym ) corresponding to the strong K sym – J sym and K sym – L anticorrelations is revealed when Δ⁢R is less than about 0.2 km, and the locations of the two peaks are sensitive to the maximum value of J sym reflecting the stiffness of E sym ⁡(ρ) above about 3 times the saturation density ρ 0 of SNM, and (4) the high-precision radius measurement for canonical NSs is more useful than that for massive ones for constraining the EOS of nucleonic matter around (2–3)⁢ρ 0 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

From Existing and New Nuclear and Astrophysical Constraints to Stringent Limits on the Equation of State of Neutron-Rich Dense Matter

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star (NS) observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. To constrain the EOS across its entire density range, this information needs to be combined consistently. However, the impact and model dependency of individual observations vary. Given their growing number, assessing the various methods is crucial to compare the respective effects on the EOS and discover potential biases. For this purpose, we present a broad compendium of different constraints and apply them individually to a large set of EOS candidates within a Bayesian framework. Specifically, we explore different ways of how chiral effective field theory and perturbative quantum chromodynamics can be used to place a likelihood on EOS candidates. We also investigate the impact of nuclear experimental constraints, as well as different radio and x-ray observations of NS masses and radii. This is augmented by reanalyses of the existing data from binary neutron star coalescences, in particular of GW170817, with improved models for the tidal waveform and kilonova light curves, which we also utilize to construct a tight upper limit of 2.39 M ⊙ on the TOV mass based on GW170817’s remnant. Our diverse set of constraints is eventually combined to obtain stringent limits on NS properties. We organize the combination in a way to distinguish between constraints where the systematic uncertainties are deemed small and those that rely on less conservative assumptions. For the former, we find the radius of the canonical 1.4 M ⊙ neutron star to be R 1.4 = 12.2 6 − 0.91 + 0.80 km and the TOV mass at M TOV = 2.2 5 − 0.22 + 0.42 M ⊙ (95% credibility). Including all the presented constraints yields R 1.4 = 12.2 0 − 0.48 + 0.50 km and M TOV = 2.3 0 − 0.20 + 0.07 M ⊙ . When comparing these limits to individual data points, we find that the quoted radius of HESS J1731-347 displays noticeable tension with other constraints. Constraining microphysical properties of the EOS proves more challenging. For instance, the symmetry energy slope is restricted to L sym = 48 − 25 + 21 MeV , where this constraint is mainly dominated by our reanalysis of the PREX-II and CREX experiment. Published by the American Physical Society 2025

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Rapid retreat of the southwestern Laurentide Ice Sheet during the Bølling-Allerød interval

The timing of Laurentide Ice Sheet deglaciation along its southwestern margin controlled the evolution of large glacial lakes and has implications for human migration into the Americas. Accurate reconstruction of the ice sheet’s retreat also constrains glacial isostatic adjustment models and is important for understanding ice-sheet sensitivity to climate forcing. Despite its significance, retreat of the southwestern Laurentide Ice Sheet (SWLIS) is poorly constrained by minimum-limiting 14 C data. We present 26 new cosmogenic 10 Be exposure ages spanning the western Interior Plains, Canada. Using a Bayesian framework, we combine these data with geomorphic mapping, 10 Be, and high-quality minimum-limiting 14 C ages to provide an updated chronology. This dataset presents an internally consistent retreat record and indicates that the initial detachment of the SWLIS from its convergence with the Cordilleran Ice Sheet began by ca. 15.0 ka, concurrent with or slightly prior to the onset of the Bølling-Allerød interval (14.7–12.9 ka) and retreated >1200 km to its Younger Dryas (YD) position in ~2500 yr. Ice-sheet stabilization at the Cree Lake Moraine facilitated a meltwater drainage route to the Arctic from glacial Lake Agassiz within the YD, but not necessarily at the beginning. Our record of deglaciation and new YD constraints demonstrate deglaciation of the Interior Plains was ~60% faster than suggested by minimum 14 C constraints alone. In conclusion, numerical modeling of this rapid retreat estimates a loss of ~3.7 m of sea-level equivalent from the SWLIS during the Bølling-Allerød interval.

58 GEOSCIENCES↗