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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Image-domain DAS 3D VSP elastic transmission tomography

SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.

Oren, Can (ORCID:000000019534265X)↗
DOE OSTI2023

Hierarchical ensemble Kalman methods with sparsity-promoting generalized gamma hyperpriors

This paper introduces a computational framework to incorporate flexible regularization techniques in ensemble Kalman methods, generalizing the iterative alternating scheme to nonlinear inverse problems. The proposed methodology approximates the maximum a posteriori (MAP) estimate of a hierarchical Bayesian model characterized by a conditionally Gaussian prior and generalized gamma hyperpriors. Suitable choices of hyperparameters yield sparsity-promoting regularization. We propose an iterative algorithm for MAP estimation, which alternates between updating the unknown with an ensemble Kalman method and updating the hyperparameters in the regularization to promote sparsity. Here, the effectiveness of our methodology is demonstrated in several computed examples, including compressed sensing and subsurface flow inverse problems.

Ensemble Kalman methods↗
DOE OSTI2025

Resummation for lattice QCD calculation of generalized parton distributions at nonzero skewness

Large-momentum effective theory (LaMET) provides an approach to directly calculate the x-dependence of generalized parton distributions (GPDs) on a Euclidean lattice through power expansion and a perturbative matching. When a parton’s momentum becomes soft, the corresponding logarithms in the matching kernel become non-negligible at higher orders of perturbation theory, which requires a resummation. But the resummation for the off-forward matrix elements at nonzero skewness ξ is difficult due to their multi-scale nature. In this work, we demonstrate that these logarithms are important only in the threshold limit, and derive the threshold factorization formula for the quasi-GPDs in LaMET. We then propose an approach to resum all the large logarithms based on the threshold factorization, which is implemented on a GPD model. We demonstrate that the LaMET prediction is reliable for [−1 + x 0 , −ξ − x 0 ] ∪ [−ξ + x 0 , ξ − x 0 ] ∪ [ξ + x 0 , 1 − x 0 ], where x 0 is a cutoff depending on hard parton momenta. Through our numerical tests with the GPD model, we demonstrate that our method is self-consistent and that the inverse matching does not spread the nonperturbative effects or power corrections to the perturbatively calculable regions.

hadronic spectroscopy↗
DOE OSTI2025

Characterization of DESI fiber assignment incompleteness effect on 2-point clustering and mitigation methods for DR1 analysis

We present an in-depth analysis of the fiber assignment incompleteness in the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). This incompleteness is caused by the restricted mobility of the robotic fiber positioner in the DESI focal plane, which limits the number of galaxies that can be observed at the same time, especially at small angular separations. As a result, the observed clustering amplitude is suppressed in a scale-dependent manner, which, if not addressed, can severely impact the inference of cosmological parameters. We discuss the methods adopted for simulating fiber assignment on mocks and data. In particular, we introduce the fast fiber assignment (FFA) emulator, which was employed to obtain the power spectrum covariance adopted for the DR1 full-shape analysis. We present the mitigation techniques, organised in two classes: measurement stage and model stage. We then use high fidelity mocks as a reference to quantify both the accuracy of the FFA emulator and the effectiveness of the different measurement-stage mitigation techniques. This complements the studies conducted in a parallel paper for the model-stage techniques, namely the θ-cut approach. We find that pairwise inverse probability (PIP) weights with angular upweighting recover the “true” clustering in all the cases considered, in both Fourier and configuration space. Notably, we present the first ever power spectrum measurement with PIP weights from real data.

cosmological simulations↗
DOE OSTI2021

Quadrupole collectivity in the neutron-rich sulfur isotopes 38,40,42,44 S

Electromagnetic transition strengths in the even-even neutron-rich sulfur isotopes 38,40,42,44 S were measured using intermediate-energy Coulomb excitation at the National Superconducting Cyclotron Laboratory. By utilizing the sensitivity of the experimental technique to E2 excitations from the ground state, the evolution of the pattern of B(E2) strengths to several low-lying 2 + states was investigated at Z = 16 from near stability to the N = 28 island of inversion. The experimental results allowed a detailed comparison with predictions from shell-model calculations using the SDPF-MU Hamiltonian, which was designed to describe collectivity in this region of the nuclear chart. While the shell-model calculations succeeded in modeling transition strengths at N = 22, 24, 26, the experimental B(E2; $0$ $^{+}_{1}$ → $2$ $^{+}_{1}$) at N = 28 was smaller than the predicted value by about a factor of 2, similar to previous observations for chlorine and argon isotopes around N = 28. The dependence of this overprediction by theory on the choice of effective charges was explored.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Provenance infrastructure for multimodal x-ray experiments and reproducible analysis

Exploring relationships between material structures and desired properties for material discovery and synthesis requires mining machine-readable databases and linking experimental data with sufficiently rich annotations. There are vast gaps both in these data sources and in the connections between them. We present a provenance-based data management and analysis framework that enables capturing, persisting, and reanalyzing experimental data from the BNL National Synchrotron Light Source II (NSLS-II). Our system leverages NSLS-II Bluesky for experimental data acquisition, captures analysis parameters, and enriches the search space with results from the scientific literature. We describe in detail the challenges and benefits of data-driven approaches when comparing computational and experimental data, such as the multiple possible scenarios of interest in often ill-posed inverse problems and missing data. Our infrastructure utilizes the NSLS-II event model and includes discovery mechanisms across multiple beamlines. A text and data mining module using natural language processing classification techniques extracts relevant information from the literature that is added to data sources. We describe a provenance acquisition and analysis rerun workflow based on a directed acyclic graph that keeps track of process and data provenance and allows to replay analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiwavelength Investigation of γ-Ray Source MGRO J1908+06 Emission Using Fermi-LAT, VERITAS and HAWC

This paper investigates the origin of the γ-ray emission from MGRO J1908+06 in the GeV–TeV energy band. By analyzing the data collected by the Fermi Large Area Telescope, the Very Energetic Radiation Imaging Telescope Array System, and High Altitude Water Cherenkov, with the addition of spectral data previously reported by LHAASO, a multiwavelength study of the morphological and spectral features of MGRO J1908+06 provides insight into the origin of the γ-ray emission. The mechanism behind the bright TeV emission is studied by constraining the magnetic field strength, the source age, and the distance through detailed broadband modeling. Both spectral shape and energy-dependent morphology support the scenario that inverse Compton emission of an evolved pulsar wind nebula associated with PSR J1907+0602 is responsible for the MGRO J1908+06 γ-ray emission with a best-fit true age of T = 22 ± 9 kyr and a magnetic field of B = 5.4 ± 0.8 μG, assuming the distance to the pulsar d PSR = 3.2 kpc.

Gamma-ray astronomy↗
DOE OSTI2024

A Spectacular Viscoelastic Model Calibration for 828/D230/Alox Generated from Legacy Sandia Data

The nonlinear viscoelastic Spectacular model is calibrated to the thermo-mechanical behavior of 828/D230/Alox with an alox volume fraction of 20 %. Legacy experimental data from Sandia’s polymer properties database (PPD) is used to calibrate the model. Based on known densities of the epoxy 828/D230 and the alox filler, the alox volume fractions listed on the PPD were likely reported incorrectly. The alox volume fractions are recalculated here. Using the recalculated alox volume fractions, the PPD contains experimental data for 828/D230/Alox with alox volume fractions of 16 %, 24 %, and 33 %, so the thermo-mechanical behavior at 20 % alox volume fraction is estimated by interpolating between the bounding cases of of 16 % and 24 %. Because the Spectacularmodel can be fairly challenging to calibrate, the calibration procedure is described in detail. Several of the calibration steps involve inverse parameter identification, where an experiment is simulated and parameters are iteratively updated until the model response matches the experimental data. As the PPD does not fully describe all experimental procedures, the experimental simulations use assumed thermal and mechanical loading rates that are typical for the viscoelastic characterization of epoxies. Spectacular uses four independent relaxation functions related to volumetric (ƒ 1 ), shear (ƒ 2 ), thermal strain (ƒ 3 ), and thermal relaxations (ƒ 4 ). The previous SPEC model form, also known as the universal_polymer model, uses two independent relaxation functions related to volumetric and thermal relaxation (ƒ ν = ƒ 1 = ƒ 3 = ƒ 4 ) and shear relaxation (ƒ s = ƒ 2 ). The two constitutive choices are briefly evaluated here, where it is found that the four relaxation function approach of Spectacular was better suited for fitting the coefficient of thermal expansion during both heating and cooling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Three-dimensional stacking of canted antiferromagnetism and pseudospin current in undoped Sr 2 IrO 4 : Symmetry analysis and microscopic model realization

Recent optical second-harmonic generation experiments observed unexpected broken spatial symmetries in the undoped spin-orbit Mott insulator Sr2 IrO4, leading to intensive debates on the nature of its ground state. Here we propose that it is a canted antiferromagnetism (CAF) with a hidden order of circulating staggered pseudospin current. Symmetry analysis shows that a proper c-axis stacking of the CAF and the pseudospin current lead to a magnetoelectric coexistence state that breaks the twofold rotation, inversion, and time-reversal symmetries, consistent with experimental observations. We construct a three-dimensional Hubbard model with spin-orbit coupling and structural distortion for the five localized 5d Wannier orbitals centered at Ir sites and demonstrate the microscopic realization of the desired coexistence state in a wide range of band parameters via a combination of self-consistent Hartree-Fock and variational calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Jet fragmentation transverse momentum distributions in pp and p-Pb collisions at $ \sqrt{s}$, $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV

Jet fragmentation transverse momentum ( j T ) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at s NN = 5 . 02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti- k T algorithm with resolution parameter R = 0 . 4 in the pseudorapidity range |η| < 0 . 25. The j T values are calculated for charged particles inside a fixed cone with a radius R = 0 . 4 around the reconstructed jet axis. The measured j T distributions are compared with a variety of parton-shower models. Herwig and P ythia 8 based models describe the data well for the higher j T region, while they underestimate the lower j T region. The j T distributions are further characterised by fitting them with a function composed of an inverse gamma function for higher j T values (called the “wide component”), related to the perturbative component of the fragmentation process, and with a Gaussian for lower j T values (called the “narrow component”), predominantly connected to the hadronisation process. The width of the Gaussian has only a weak dependence on jet transverse momentum, while that of the inverse gamma function increases with increasing jet transverse momentum. For the narrow component, the measured trends are successfully described by all models except for Herwig. For the wide component, Herwig and PYTHIA 8 based models slightly underestimate the data for the higher jet transverse momentum region. These measurements set constraints on models of jet fragmentation and hadronisation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Establishing a process-structure-property-performance framework for SLS additive manufacturing through integrated multiscale modeling

This study presents a comprehensive suite of high-fidelity computational models that integrate multiscale and multiphysics simulations to capture the full Selective Laser Sintering (SLS) additive manufacturing process—from initial melting and solidification to mechanical response under external loads. Process simulations are linked with mechanical analysis through Representative Volume Elements (RVEs), establishing a process-structure–property-performance framework. The interaction between laser light and polyamide 12 (PA12) powder is modeled, accounting for laser characteristics and the optical, thermal, and geometrical properties of the powder. The heat source is incorporated into a heat transfer model, coupled with crystallization kinetics and densification models to predict material density and crystallinity. The porosity distribution from the densification model and crystallinity interpolated from experimental data are used to construct the RVEs. A multi-mechanism constitutive model is then calibrated using mechanical tests to predict the stress–strain response. Simulation results show good agreement with experimental data in terms of porosity, crystallinity, and mechanical performance when sufficient laser power (62 W or higher) is used. This research supports the inverse design of 3D-printed structures by introducing a high-fidelity framework that combines multiscale and multiphysics modeling with experimental calibration for predictive and performance-driven additive manufacturing.

SLS↗
DOE OSTI2020

Structure of 30 Mg explored via in-beam γ-ray spectroscopy

In the island of inversion, ground states of neutron-rich sd-shell nuclei exhibit strong admixtures of intruder configurations from the fp shell. The nucleus 30 Mg, located at the boundary of the island of inversion, serves as a cornerstone to track the structural evolution as one approaches this region. Spin-parity assignments for excited states in 30 Mg, especially negative-parity levels, have yet to be established. In the present work, the nuclear structure of 30Mg was investigated by in-beam gamma-ray spectroscopy mainly focusing on firm spin-parity determinations. High-intensity rare-isotope beams of 31 Mg, 32 Mg, 34 Si, and 35 P bombarded a Be target to induce nucleon removal reactions populating states in 30 Mg. Gamma rays were detected by the state-of-the-art gamma-ray tracking array GRETINA. For the direct one-neutron removal reaction, final-state exclusive cross sections and parallel momentum distributions were deduced. Multi-nucleon removal reactions from different projectiles were exploited to gain complementary information. With the aid of the parallel momentum distributions, an updated level scheme with revised spin-parity assignments was constructed. Spectroscopic factors associated with each state were also deduced. Results were confronted with large-scale shell-model calculations using two different effective interactions, showing excellent agreement with the present level scheme. Furthermore, a marked difference in the spectroscopic factors indicates that the full delineation of the transition into the island of inversion remains a challenge for theoretical models.

20 ≤ A ≤ 38↗
DOE OSTI2025

Twisting the Hubbard model into the momentum-mixing Hatsugai–Kohmoto model

The Hubbard model is a standard theoretical tool for studying materials with strong electron–electron interactions, such as cuprate superconductors. Unfortunately, interaction-driven phenomena, such as a transition into the strongly correlated Mott insulator phase, are difficult to treat with established theoretical techniques. However, the exactly solvable Hatsugai–Kohmoto model displays similar Mott physics. In this work we show how the Hatsugai–Kohmoto model can be deformed continuously into the Hubbard model. The trick is to systematically reintroduce all the momentum mixing that the original Hatsugai–Kohmoto model omits. This can be accomplished by grouping n momenta into a cell and hybridizing them, resulting in the momentum-mixing Hatsugai–Kohmoto model. We recover the Bethe ansatz ground-state energy of the one-dimensional Hubbard model to within 1% from only ten mixed momenta. Overall, the convergence scales as 1/n2 as opposed to the inverse linear behaviour of standard finite-cluster techniques. Our results for a square lattice reproduce all the known features from state-of-the-art simulations also with only a few mixed momenta. Consequently, we believe that the momentum-mixing Hatsugai–Kohmoto model offers an alternative tool for strongly correlated quantum matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Statistical Interpolation Code for Ocean Analysis and Forecasting

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .

Srinivasan, Ashwanth↗
DOE OSTI2020

Neutrinoless double beta decay versus other probes of heavy sterile neutrinos

We make a comparative study of the neutrinoless double beta decay constraints on heavy sterile neutrinos versus other direct and indirect constraints from both lepton number conserving and violating processes, as a sensitive probe of the extent of lepton number violation and possible interference effects in the sterile sector. We introduce a phenomenological parametrisation of the simplified one-generation seesaw model with one active and two sterile neutrino states in terms of experimentally measurable quantities, such as active-sterile neutrino mixing angles, CP phases, masses and mass splittings. This simple parametrisation enables us to analytically derive a spectrum of possible scenarios between the canonical seesaw with purely Majorana heavy neutrinos and inverse seesaw with pseudo-Dirac ones. We then go on to constrain the simplified parameters of this model from various experiments at the energy, intensity and cosmic frontiers. We emphasise that the constraints from lepton number violating processes strongly depend on the mass splitting between the two sterile states and the relative CP phase between them. This is particularly relevant for neutrinoless double beta decay, which is weakened for small mass splitting and opposite CP parities between the sterile states. On the other hand, neutrinoless double beta decay is especially sensitive for Majorana sterile neutrinos with masses around 0.1-10 GeV.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Analytic continuation of noisy data using Adams Bashforth residual neural network

We propose a data-driven learning framework for the analytic continuation problem in numerical quantum many-body physics. Designing an accurate and efficient framework for the analytic continuation of imaginary time using computational data is a grand challenge that has hindered meaningful links with experimental data. The standard Maximum Entropy (MaxEnt)-based method is limited by the quality of the computational data and the availability of prior information. Also, the MaxEnt is not able to solve the inversion problem under high level of noise in the data. Here we introduce a novel learning model for the analytic continuation problem using a Adams-Bashforth residual neural network (AB-ResNet). Additionally, the advantage of this deep learning network is that it is model independent and, therefore, does not require prior information concerning the quantity of interest given by the spectral function. More importantly, the ResNet-based model achieves higher accuracy than MaxEnt for data with higher level of noise. Finally, numerical examples show that the developed AB-ResNet is able to recover the spectral function with accuracy comparable to MaxEnt where the noise level is relatively small.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics-informed Karhunen-Loeve and Neural Network Approximations for Solving Inverse Differential Equation Problems

Here we present the PI-CKL-NN method for parameter estimation in differential equation (DE) models given sparse measurements of the parameters and states. In the proposed approach, the space- or time-dependent parameters are approximated by Karhunen-Loeve (KL) expansions that are conditioned on the parameters’ measurements, and the states are approximated by deep neural networks (DNNs). The unknown weights in the KL expansions and DNNs are found my minimizing the cost function that enforces the measurements of the states the DE constraint. Regularization is achieved by adding the l2 norm of the conditional KL coefficients into the loss function. Our approach assumes that the parameter fields are correlated in space or time and enforces the statistical knowledge (the mean and the covariance function) in addition to the DE constraints and measurements as opposed to the physics-informed neural network (PINN) and other similar physics-informed machine learning methods where only DE constraints and data are used for parameter estimation. We use the PI-CKL-NN method for parameter estimation in an ordinary differential equation with an unknown time-dependent parameter and the one- and two-dimensional partial differential diffusion equations with unknown space-dependent diffusion coefficients. We also demonstrate that PI-CKL-NN is more accurate than the PINN method, especially when the observations of the parameters are very sparse

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Current and Future Global Lake Methane Emissions: A Process‐Based Modeling Analysis

Abstract Freshwater ecosystem contributions to the global methane budget remains the most uncertain among natural sources. With warming and accompanying carbon release from thawed permafrost and thermokarst lake expansion, the increase of methane emissions could be large. However, the impact and relative importance of various factors related to warming remain uncertain. Based on diverse lake characteristics incorporated in modeling and observational data, we calibrate and verify a lake biogeochemistry model. The model is then applied to estimate global lake methane emissions and examine the impacts of temperature increase for the first and the last decades of the 21st century under different climate scenarios. We find that current emissions are 24.0 ± 8.4 Tg CH 4 yr −1 from lakes larger than 0.1 km 2 , accounting for 11% of the global total natural source as estimated based on atmospheric inversion. Future projections under the RCP8.5 scenario suggest a 58%–86% growth in emissions from lakes. Our model sensitivity analysis indicates that additional carbon substrates from thawing permafrost may enhance methane production under warming in the Arctic. Warming enhanced methane oxidation in lake water can be an effective sink to reduce the net release from global lakes.

54 ENVIRONMENTAL SCIENCES↗