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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Realization of the Haldane-Kane-Mele Model in a System of Localized Spins

We study a spin Hamiltonian for spin-orbit-coupled ferromagnets on the honeycomb lattice. At sufficiently low temperatures supporting the ordered phase, the effective Hamiltonian for magnons, the quanta of spin-wave excitations, is shown to be equivalent to the Haldane model for electrons, which indicates the nontrivial topology of the band and the existence of the associated edge state. At high temperatures comparable to the ferromagnetic-exchange strength, we take the Schwinger-boson representation of spins, in which the mean-field spinon band forms a bosonic counterpart of the Kane-Mele model. The nontrivial geometry of the spinon band can be inferred by detecting the spin Nernst effect. A feasible experimental realization of the spin Hamiltonian is proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Structure Slicing: Extending Logical Regions with Fields, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Applications on modern supercomputers are increasingly limited by the cost of data movement, but mainstream programming systems have few abstractions for describing the structure of a program's data. Consequently, the burden of managing data movement, placement, and layout currently falls primarily upon the programmer. To address this problem we previously proposed a data model based on logical regions and described Legion, a programming system incorporating logical regions. In this paper, we present structure slicing, which incorporates fields into the logical region data model. We show that structure slicing enables Legion to automatically infer task parallelism from field non-interference, decouple the specification of data usage from layout, and reduce the overall amount of data moved. We demonstrate that structure slicing enables both strong and weak scaling of three Legion applications including S3D, a production combustion simulation that uses logical regions with thousands of fields, with speedups of up to 3.68X over a vectorized CPU-only Fortran implementation and 1.88X over an independently hand-tuned OpenACC code.

Computer Science↗
DOE OSTI2014

SU-E-J-128: 3D Surface Reconstruction of a Patient Using Epipolar Geometry

Purpose: To obtain a 3D surface data of a patient in a non-invasive way can substantially reduce the effort for the registration of patient in radiation therapy. To achieve this goal, we introduced the multiple view stereo technique, which is known to be used in a 'photo tourism' on the internet. Methods: 70 Images were taken with a digital single-lens reflex camera from different angles and positions. The camera positions and angles were inferred later in the reconstruction step. A sparse 3D reconstruction model was locating by SIFT features, which is robust for rotation and shift variance, in each image. We then found a set of correspondences between pairs of images by computing the fundamental matrix using the eight-point algorithm with RANSAC. After the pair matching, we optimized the parameter including camera positions to minimize the reprojection error by use of bundle adjustment technique (non-linear optimization). As a final step, we performed dense reconstruction and associate a color with each point using the library of PMVS. Results: Surface data were reconstructed well by visual inspection. The human skin is reconstructed well, althogh the reconstruction was time-consuming for direct use in daily clinical practice. Conclusion: 3D reconstruction using multi view stereo geometry is a promising tool for reducing the effort of patient setup. This work was supported by JSPS KAKENHI(25861128)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Surface uplift and time-dependent seismic hazard due to fluid injection in eastern Texas

Observations that unequivocally link seismicity and wastewater injection are scarce. We show that wastewater injection in eastern Texas causes uplift, detectable in radar interferometric data up to >8 kilometers from the wells. Using measurements of uplift, reported injection data, and a poroelastic model, we computed the crustal strain and pore pressure. We infer that an increase of >1 megapascal in pore pressure in rocks with low compressibility triggers earthquakes, including the 4.8–moment magnitude event that occurred on 17 May 2012, the largest earthquake recorded in eastern Texas. Seismic activity increased even while injection rates declined, owing to diffusion of pore pressure from earlier periods with higher injection rates. Finally, induced seismicity potential is suppressed where tight confining formations prevent pore pressure from propagating into crystalline basement rocks.

58 GEOSCIENCES↗
DOE OSTI2023

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗
DOE OSTI2016

(Active) Learning on Groups of Data with Information-Theoretic Estimators

A wide range of machine learning problems, including astronomical inference about galaxy clusters, scene classification, parametric statistical inference, and predictions of public opinion, can be well-modeled as learning a function on (samples from) distributions. This project explores problems in learning such functions via kernel methods, particularly for large-scale problems. When learning from large numbers of distributions, the computation of typical methods scales between quadratically and cubically, and so they are not amenable to large datasets. We investigate the approach of approximate embeddings into Euclidean spaces such that inner products in the embedding space approximate kernel values between the source distributions. We first improve the understanding of the workhorse methods of random Fourier features: we show that of the two approaches in common usage, one is strictly superior. We then present a new embedding for a class of information-theoretic distribution distances, and evaluate it and existing embeddings on several real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Opacity: A window into High Energy Density Plasma Physics

Opacity is a fundamental property of matter and knowledge of it is required to understand the transport of radiation through a material. Exact knowledge of opacity is crucial in many fields such as astrophysics, which recently produced a white paper on the current state of astrophysical opacities [1], plasma physics, including inertial confinement fusion (ICF) and magnetic confinement fusion, high energy density physics (HEDP), the semiconductor industry [2] and stockpile stewardship. In industry, the accurate knowledge of the opacity of tin is important for extreme ultraviolet (EUV) lithography and the development of processors with smaller components. In astrophysics, Cepheid variable stars are used as “standard candles” for determining the distance to objects. Opacities of these stars are required to understand the relationship between their periodicity, luminosity and distance. Our sun is the most studied star and provides the basis of our understanding for other stars. Yet in significant ways the sun is poorly understood. In recent years, there has been a debate over the abundances of heavy elements (Z > 2) in the solar interior. Current solar atmosphere models [3] find a significantly lower abundance for C, N, and O compared to models used roughly a decade ago. Although solar evolution models have matured, a discrepancy still exists between spectroscopic solar models (which require opacities) with updated abundances, and solar properties inferred from helioseismology and neutrinos [4]. Despite its necessity, opacity is not solved, and is an open and active area of research needed for improving our understanding of plasma physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Sequential Monte Carlo for Cut-Bayesian Posterior Computation

We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The criterion of supernova explosion revisited: The mass accretion history

By performing neutrino-radiation hydrodynamic simulations in spherical symmetry (1D) and axial symmetry (2D) with different progenitor models by Woosley and Heger from 12 to 100 M{sub ⊙}, we find that all 1D runs fail to produce an explosion and several 2D runs succeed. The difference in the shock evolutions for different progenitors can be interpreted by the difference in their mass accretion histories, which are in turn determined by the density structures of progenitors. The mass accretion history has two phases in the majority of the models: the earlier phase, in which the mass accretion rate is high and rapidly decreasing, and the later phase, with a low and almost constant accretion rate. They are separated by the so-called turning point, the origin of which is a change of the accreting layer. We argue that shock revival will most likely occur around the turning point and hence that its location in the M-dot --L{sub ν} plane will be a good measure for the possibility of shock revival: if the turning point lies above the critical curve and the system stays there for a long time, shock revival will obtain. In addition, we develop a phenomenological model to approximately evaluate the trajectories in the M-dot --L{sub ν} plane, which, after calibrating free parameters by a small number of 1D simulations, reproduces the location of the turning point reasonably well by using the initial density structure of progenitor alone. We suggest the application of the phenomenological model to a large collection of progenitors in order to infer without simulations which ones are more likely to explode.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

How Do Type Ia Supernova Nebular Spectra Depend on Explosion Properties? Insights from Systematic Non-LTE Modeling

We present a radiative transfer code to model the nebular phase spectra of supernovae (SNe) in non-LTE (NLTE). We apply it to a systematic study of SNe Ia using parameterized 1D models and show how nebular spectral features depend on key physical parameters, such as the time since explosion, total ejecta mass, kinetic energy, radial density profile, and the masses of {sup 56}Ni, intermediate-mass elements, and stable iron-group elements. We also quantify the impact of uncertainties in atomic data inputs. We find the following. (1) The main features of SN Ia nebular spectra are relatively insensitive to most physical parameters. Degeneracy among parameters precludes a unique determination of the ejecta properties from spectral fitting. In particular, features can be equally well fit with generic Chandrasekhar mass (M{sub ch}), sub-M{sub Ch}, and super-M{sub Ch} models. (2) A sizable (≳0.1 M{sub ⊙}) central region of stable iron-group elements, often claimed as evidence for M{sub Ch} models, is not essential to fit the optical spectra and may produce an unusual flat-top [Co iii] profile. (3) The strength of [S iii] emission near 9500 Å can provide a useful diagnostic of explosion nucleosynthesis. (4) Substantial amounts (≳0.1 M{sub ⊙}) of unburned C/O mixed throughout the ejecta produce [O iii] emission not seen in observations. (5) Shifts in the wavelength of line peaks can arise from line-blending effects. (6) The steepness of the ejecta density profile affects the line shapes, offering a constraint on explosion models. (7) Uncertainties in atomic data limit the ability to infer physical parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Challenges Ahead for Multimessenger Analyses of Gravitational Waves and Kilonova: A Case Study on GW190425

In recent years, there have been significant advances in multimessenger astronomy due to the discovery of the first, and so far only confirmed, gravitational wave event with a simultaneous electromagnetic (EM) counterpart, as well as improvements in numerical simulations, gravitational wave (GW) detectors, and transient astronomy. This has led to the exciting possibility of performing joint analyses of the GW and EM data, providing additional constraints on fundamental properties of the binary progenitor and merger remnant. Here, in this work, we present a new Bayesian framework that allows inference of these properties, while taking into account the systematic modeling uncertainties that arise when mapping from GW binary progenitor properties to photometric light curves. We extend the relative binning method presented in Zackay et al. to include extrinsic GW parameters for fast analysis of the GW signal. The focus of our EM framework is on light curves arising from r-process nucleosynthesis in the ejected material during and after merger, the so-called kilonova, and particularly on black hole–neutron star systems. As a case study, we examine the recent detection of GW190425, where the primary object is consistent with being either a black hole or a neutron star. We show quantitatively how improved mapping between binary progenitor and outflow properties, and/or an increase in EM data quantity and quality are required in order to break degeneracies in the fundamental source parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Bayesian inference for high confidence signal validation and sensor calibration assessment - 328

Online Monitoring (OLM) systems has been investigated for applications in the nuclear industry for the past two decades. These systems offer economic and technological advantages over the currently implemented time based testing methods for sensors and associated instrumentations. The advantages offered not only augment economic viability of current nuclear reactors but will be an inevitable change called for to suit the technologies of the advanced reactors (Gen III+). Irrespective, the acceptance of OLM into the regulatory structure in the U.S. is still hindered by technical and structural clarity on uncertainty quantification used by these systems. A Bayesian inference technique is implemented in the form of a residual model to quantify uncertainty in OLM predictions. The developed system used Gaussian processes to represent the OLM predictions and model inadequacy, the stationary representation of model based uncertainty. In addition, model selection techniques are used in conjunction with numerical methods such as Genetic Algorithm (GA) and Gibb's sampler to add robustness to the estimation model inadequacy, particularly in the presence of drifts and faulted conditions. The prediction model developed herein find applicability in fault detection, obtaining real values of processes after the onset of faults etc. Further development looks into the extension of current method for a multiple output scale implementation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

The kinetic Sunyaev-Zel'dovich effect from the diffuse gas in the Local Group

Since the Local Group (LG) of galaxies moves with a bulk velocity with respect to the cosmic microwave background radiation (CMB), free electrons in its gaseous halo should imprint large-scale non-primordial temperature shifts in the CMB via the kinetic Sunyaev-Zel'dovich (kSZ) effect. By modeling the distribution of gas in the LG halo and using its inferred velocity with respect to the CMB, we calculate the resulting kSZ signal from the diffuse LG medium. We find that it is dominated by a hot spot ∼ 10° in size in the direction of M31, where the optical depth of free electrons is the greatest. By performing a correlation analysis, we find no statistical evidence that the kSZ signal from model of the LG halo is embedded in the CMB temperature map measured by the Planck satellite. We constrain the amount of mass in the LG halo by limiting the kSZ temperature shift around the hot spot to be smaller than the observed temperature shift in the Planck map. We find the tightest constraints for models where the halo mass is highly concentrated, with the mass limited to roughly 2.5–5 × 10{sup 12}M{sub ⊙}, but note that halos with such high concentrations are rare.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

The detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method, we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and point-spread function classes. Using three energy bins spanning 0.3–1, 1–3, and 3–10 GeV, we identify 270{sub −10}{sup +30} point sources inside a 40{sup ∘}×40{sup ∘} region around the NGP above our point-source inclusion limit of 3×10{sup −11} cm{sup −2} s{sup −1} sr{sup −1} GeV{sup −1} at the 1–3 GeV energy bin. Modeling the flux distribution as a power law, we infer the slope to be −1.92{sub −0.05}{sup +0.07} and estimate the contribution of point sources to the total emission as 18{sub −2}{sup +2}%. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Super-CMB fluctuations and the Hubble tension

We study the covariance in the angular power spectrum estimates of CMB fluctuations when the primordial fluctuations are non-Gaussian. The non-Gaussian covariance comes from a nonzero connected four-point correlation function -- or the trispectrum in Fourier space -- and can be large when long-wavelength (super-CMB) modes are strongly coupled to short-wavelength modes. The effect of such non-Gaussian covariance can be modeled through additional freedom in the theoretical CMB angular power spectrum and can lead to different inferred values of the standard cosmological parameters relative to those in ΛCDM. Taking the collapsed limit of the primordial trispectrum in the quasi-single field inflation model as an example, we study how the six standard ΛCDM parameters shift when two additional parameters describing the trispectrum are allowed. The reduced statistical significance of the Hubble tension in the extended model allows us to combine the {\it Planck} temperature data and the type Ia supernovae data from Panstarrs with the distance-ladder measurement of the Hubble constant. Here, this combination of data shows strong evidence for a primordial trispectrum-induced non-Gaussian covariance, with a likelihood improvement of Δχ 2 ≈ –15 (with two additional parameters) relative to ΛCDM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2023

Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weak lensing with HSC and SDSS using the emulator based halo model

We present cosmology results from a blinded joint analysis of cosmic shear, galaxy-galaxy weak lensing, and projected galaxy clustering, measured from the Hyper Suprime-Cam three-year (HSC-Y3) shape catalog and the Sloan Digital Sky Survey (SDSS) DR11 spectroscopic galaxy catalog—a cosmology analysis. We define luminosity-cut, and therefore nearly volume-limited, samples of SDSS galaxies to serve as the tracers of w p and as the lens samples for Δ⁢Σ in three spectroscopic redshift bins spanning the range 0.15± and Δ⁢Σ measurements, we use a single sample of about seven million source galaxies over 416 deg 2 , selected from HSC-Y3 based on having photometric redshifts (photo-z) greater than 0.75. The deep, high-quality HSC-Y3 data enable significant detections of the Δ⁢Σ signals, with integrated signal-to-noise ratio S/N~24 in the range 3≤R/[ℎ –1 Mpc]≤30 over the three lens samples. ξ ± has S/N ~19 in the range 8' ≤$\theta$ ≤5⁢0' and 30' ≤$\theta$ ≤15⁢0' for ξ + and ξ – , respectively. For cosmological parameter inference, we use the dark emulator package, combined with a halo occupation distribution prescription for the relation between galaxies and halos, to model w p and Δ⁢Σ down to quasinonlinear scales, and we estimate cosmological parameters after marginalizing over nuisance parameters. In our baseline analysis we employ an uninformative flat prior of the residual photo-z error, given by Π⁡(Δz ph )=$U$⁡(–1,1), to model a residual bias in the mean redshift of HSC source galaxies. Comparing the relative lensing amplitudes for Δ⁢Σ in the three redshift bins and for ξ ± with the single HSC source galaxy sample allows us to calibrate the photo-z parameter Δ⁢z ph to the precision of σ⁡(Δ⁢z ph )≃0.09. With these methods, we obtain a robust constraint on the cosmological parameters for the flat Λ⁢CDM model: S 8 =σ 8 ⁢(Ω m /0.3) 0.5 =$0.763^{+0.040}_{–0.036}$, or the best-constrained parameter given by $S^{'}_{8}$ = σ 8 ⁢(Ω m /0.3) 0.22 =0.721±0.028, determined with about 4% fractional precision. Based on multidimensional tension metrics, HSC-Y3 data exhibits about 2.5σ tension with the cosmological constraint inferred by Planck for the Λ⁢CDM model, and hints at a nonzero residual photo-z bias implying that the true mean redshift of the HSC galaxies at z ≳0.75 is higher than that implied by the original photo-z estimates.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape: Modeling Archive

This modeling archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025). This archive contains model input files and outputs from landscape-scale simulations conducted using ELM, the land model component of the Department of Energy’s Energy Exascale Earth System Model (E3SM), at the Council NGEE Arctic field site (Council Road mile marker 71) on Alaska’s Seward Peninsula. Input data and model output from two sets of ELM simulations are provided. The first set of simulations were conducted with the two default ELM Arctic plant functional types (PFTs; broadleaf deciduous boreal shrub and a C3 grass) and the second set of simulations were conducted with a set of nine Arctic-specific PFTs including nonvascular mosses and lichens, graminoids, forbs, evergreen dwarf shrubs, three height classes of deciduous shrubs (dwarf, low, and low to tall), and deciduous alder shrubs (Sulman et al., 2021). Parameter names and major parameter changes in the Arctic-specific PFT configuration are described in Sulman et al. (2021) and archived in the Sulman et al. (2021) dataset (see below). Simulations were spatially explicit, covering an approximately 6.4X3.3 km domain at the Council site with a spatial resolution of 100 m for a total of 2,112 simulated grid cells under each ELM PFT configuration. The modeling archive contains meteorological forcing (seven *.nc files and one *.txt file), a domain definition file (one *.nc files), land surface configuration files (two *.nc files), parameter files (two *.nc files), annual ELM output files spanning 1980-2014 (68 *.nc files), and a User’s Guide (*pdf file). Additional information on the provided files is in the “Modeling Archive Contents” section of the User’s Guide. Model outputs are aggregated to the column scale (i.e. PFT-specific outputs are not provided here).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗