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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Magnetic dipole γ-ray strength functions in the crossover from spherical to deformed neodymium isotopes

We calculate the magnetic dipole $\gamma$-ray strength functions in a chain of even-mass neodymium isotopes $^{144-152}$Nd in the framework of the configuration-interaction (CI) shell model. We infer the strength function by applying the maximum entropy method (MEM) to the exact imaginary-time response function calculated with the shell-model Monte Carlo (SMMC) method. The success of the MEM depends on the choice of a good strength function as a prior distribution. We investigate two choices for the prior strength function: the static path approximation (SPA) and the quasiparticle random-phase approximation (QRPA). We find that the QRPA is a better approximation at low temperatures (i.e., near the ground state), while the SPA is a better choice at finite temperatures. We identify a low-energy enhancement (LEE) in the MEM deexcitation $M1$ strength functions of the even-mass neodymium isotopes and compare with recent experimental results for the total deexcitation $\gamma$-ray strength functions. The LEE is already seen in the SPA strength function but not in the QRPA strength function, indicating the importance of large-amplitude static fluctuations around the mean field in reproducing the LEE. Our method is currently the only one which can reproduce LEE in heavy open-shell nuclei where conventional CI shell model calculations are prohibited. With the onset of deformation as number of neutrons increases along the chain of neodymium isotopes, we observe that some of the LEE strength transfers to a low-energy excitation, which we interpret as a finite-temperature ``scissors'' mode. Here, we also observe a finite-temperature spin-flip mode.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from an MCMC analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, ($\Omega_m$ and $\sigma_8$), and richness-mass relation parameters. We also perform out-of-domain tests with observables constructed from galaxy cluster-sized halos in the Quijote simulations. Again, the SBI and MCMC results have comparable posteriors, with similar uncertainties and biases. Unsurprisingly, upon evaluating the SBI method on thousands of simulated data vectors that span the parameter space, SBI exhibits worsened posterior calibration metrics in the out-of-domain application. We note that such calibration tests with MCMC is less computationally feasible and highlight the potential use of SBI to stress-test limitations of analytical models, such as in the use for constructing models for inference with MCMC.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Radial diffusion in the Uranian radiatian belts - Inferences from satellite absorption loss models

Low-energy charged particle (LECP) phase space density profiles available from the Voyager/1986 Uranus encounter are analyzed, using solutions of the time-averaged radial diffusion equation for charged particle transport in a dipolar planetary magnetic field. Profiles for lower-energy protons and electrons are first analyzed to infer radial diffusion rate as a function of L, assuming that satellite absorption is the dominant loss process and local sources for these particles are negligible. Satellite macrosignatures present in the experimentally derived profiles are approximately reproduced in several cases, lending credence to the loss model and indicating that magnetospheric distributed losses are not as rapid as satellite absorption near the minimum satellite L shells for the particles. Diffusion rates and L dependences are found to be similar to those previously inferred in the inner Jovian magnetosphere (Thomsen et al., 1977) and for the inner Saturnian magnetosphere (Hood, 1985). Profiles for higher energy electrons and protons are also analyzed using solutions that allow for the existence of significant particle sources as well as sinks. Possible implications for radial diffusion mechanisms in the Uranian radiation belts are discussed.

Hood, L. L.↗
DOE OSTI2015

Modelling twentieth century global ocean circulation and iceberg flux at 48°N: implications for west Greenland iceberg discharge

We have used a coupled ocean–iceberg model to study the variation in global ocean circulation and North Atlantic iceberg flux from 1900 to 2008. The latter component of the study focused particularly on Greenland icebergs feeding into the Labrador Current and past Newfoundland. The model was forced with daily heat, freshwater and wind fluxes from the Twentieth Century Reanalysis. The reanalysis heat fluxes were shown to be offset from the, shorter, NCEP reanalysis and a grid-point correction was applied to this component of the forcing. The model produces a generally realistic ocean circulation, although with an enhanced Atlantic Meridional Overturning largely due to the forcing. The modelled iceberg flux at 48°N is well correlated with the long-term observed flux when using a modelled iceberg discharge that varies in a similar fashion to the highly variable observed flux at 48°N. From this model we infer changes in the spatial and temporal variability of iceberg calving from western Greenland. During the first third of the twentieth century the majority of modelled icebergs reaching 48°N derive from southern Greenland, while only after 1930 is the traditional perspective of a majority of such icebergs originating from Baffin Bay consistent with model results. Decadal-scale changes in the dominant regional sources are found, with oscillations between western Greenland and northern Baffin Bay. The latter origin was modelled to be most important in the last third of the twentieth century, although west Greenland sources have increased in importance in recent years. The model correctly reproduces the pronounced late spring peak in flux at 48°N for southern Greenland icebergs, but has an approximately six month offset for icebergs from Baffin Bay, most likely due to resolution issues leading to model icebergs not being delayed in shallow coastal waters, whereas in reality they may be grounded for some time or trapped in coastal sea-ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Optimal Proactive Vehicle Relocation for On-Demand Mobility Service with Deep Convolution-LSTM Network

This study develops an innovative model for inferring optimal proactive relocation strategy for empty vehicles of on-demand mobility service (OMS) directly from past observations of passenger demand. The output of our model is a row stochastic relocation matrix which gives the probability that an empty vehicle should be relocated from location i to location j at any given time. The matrix itself is high-dimensional and therefore challenging to obtain accurate estimation with traditional machine learning approach. Our model proposes the low-rank approximation of the relocation matrix, and introduces the deep convolution-LSTM network which extracts low-level spatial and temporal features from past demand observations for best restoration of the actual low rank approximation vector. The model is trained by deriving the optimal matching strategies from one year of real-world trip data. The results show that our model is able to predict the low-rank approximation of the relocation matrix with 4% of mean absolute error, may reduce the relocation cost by 20%-45% as compared to the real-world observations and is 50% optimal as compared to the global optimal matching with perfect information.

Lei, Zengxiang↗
DOE OSTI2020

Concurrent Relaxation through Accelerated Deep Learning

CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.

Zieb, KristoferJ.↗
DOE OSTI2018

Application of Bound-to-Bound Data Collaboration approach for development and uncertainty quantification of a reduced char combustion model

The development of efficient industrial oxy-coal boilers can be significantly aided by Computational Fluid Dynamics (CFD) tools, as far as fidelity in modeling coal combustion is also complemented by feasible computational costs. Reduced and predictive models are the most suitable for this application scale. Reduced models feature predictivity when they are validated against a broad range of experiments and targeted by Uncertainty Quantification (UQ) procedures. This work proposes a numerical procedure that uses Bound-to-Bound Data Collaboration (B2B-DC) to derive a reduced char combustion model describing transport phenomena and reactions between char carbon and O 2 , CO 2 and H 2 O, in both conventional and oxy-conditions. The approach determines the consistency between a numerical model and an experimental dataset. The latter is made up of the experiments carried out in an optically accessible laminar entrained flow reactor, operated by Sandia National Laboratories. The procedure follows five steps towards predictive modeling capability, namely: quantification of the uncertainty in the experiments, via instrument verification and modeling; development of a physics model and continuous improvement of its fidelity, via model-form uncertainty; identification of the uncertain and most sensitive parameters and of their prior bounds; sampling of the initial uncertain parameter space and training of a surrogate model; validation of the physics model via inference from the data. The last step, also known as inverse problem, is performed by applying the Bound-to-Bound Data Collaboration approach. A char combustion model is found consistent with the experimental data and its validity stands for conventional and oxy-combustion conditions. It accounts for heterogeneous reactions at the particle surface, mass transport of species in the particle boundary layer, pore diffusion and surface area changes. The consistent reduced model overcomes the differences in mass transport and kinetics observed in the experimental campaign. In conclusion, a reduction of the initial degree of uncertainty in both model and experiments is achieved.

42 ENGINEERING↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ren, Huiying↗
NASA NTRS2005

Changes in the Temperature of Saturn's Stratosphere from 2002 to 2004 and Direct Evidence of a Mesopause

The state of a planetary stratosphere is intricately linked to the radiation it receives from its host star. The stellar flux impinging on the the planetary stratosphere affects the temperature and chemistry occurring there. The revolution of the planet about its orbit coupled to the planets axial tilt can cause variations in the amount of flux captured by the planet at a given latitude over the period of a year even if the flux output of the star is constant. This variation of stellar flux at a given latitude during the orbit of a planet is what causes seasons. Saturn, with its axial tilt of 27 degrees, experiences seasonal forcing much like the Earth, albeit with far less solar flux and a year equal to 30 Earth years. In an attempt to measure the effect seasonal forcing has on the temperature structure of Saturn's stratosphere, we have started a long term observing program. By observing Saturn with a high-resolution mid-infrared spectrograph in the v(sub 4) band of methane, we can infer the stratospheric temperature between the pressure levels of 10 and 0.01 mbar. We present latitudinally resolved observations of Saturn's southern hemisphere from 2002 and 2004. We have analyzed the data using a line-by-line radiative transfer model and inferred stratospheric temperatures and winds. We compare the temperatures from the two observing runs and the predictions of a stratospheric seasonal climate model. The zonal winds in the southern hemisphere, between the 10 and 0.01 mbar pressure levels, will be shown and discussed.

Greathouse, Thomas K.↗
DOE OSTI2013

PHIBSS: MOLECULAR GAS, EXTINCTION, STAR FORMATION, AND KINEMATICS IN THE z = 1.5 STAR-FORMING GALAXY EGS13011166

We report matched resolution imaging spectroscopy of the CO 3-2 line (with the IRAM Plateau de Bure millimeter interferometer) and of the H{alpha} line (with LUCI at the Large Binocular Telescope) in the massive z = 1.53 main-sequence galaxy EGS 13011166, as part of the ''Plateau de Bure high-z, blue-sequence survey'' (PHIBSS: Tacconi et al.). We combine these data with Hubble Space Telescope V-I-J-H-band maps to derive spatially resolved distributions of stellar surface density, star formation rate, molecular gas surface density, optical extinction, and gas kinematics. The spatial distribution and kinematics of the ionized and molecular gas are remarkably similar and are well modeled by a turbulent, globally Toomre unstable, rotating disk. The stellar surface density distribution is smoother than the clumpy rest-frame UV/optical light distribution and peaks in an obscured, star-forming massive bulge near the dynamical center. The molecular gas surface density and the effective optical screen extinction track each other and are well modeled by a ''mixed'' extinction model. The inferred slope of the spatially resolved molecular gas to star formation rate relation, N = dlog{Sigma}{sub starform}/dlog{Sigma}{sub molgas}, depends strongly on the adopted extinction model, and can vary from 0.8 to 1.7. For the preferred mixed dust-gas model, we find N = 1.14 {+-} 0.1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Multi-frequency Scatter Broadening Evolution of Pulsars. I

We present multi-wavelength scatter broadening observations of 47 pulsars made with the Giant Metre-wave Radio Telescope (GMRT), Ooty Radio Telescope (ORT), and Long Wavelength Array (LWA). The GMRT observations were made in the phased array mode at 148, 234, and 610 MHz and the ORT observations at 327 MHz. The LWA data sets were obtained from the LWA pulsar data archive. The broadening of each pulsar as a function of observing frequency provides the frequency scaling index, α. The estimations of α have been obtained for 39 pulsars and include entirely new estimates for 31 pulsars. This study increases the total sample of pulsars available with α estimates by ∼50%. The overall distribution of α with the dispersion measure (DM) of the pulsar shows interesting variations, which are consistent with earlier studies. However, for a given value of DM, a range of α values are observed, indicating the characteristic turbulence along each line of sight. For each pulsar, the estimated level of turbulence, C{sub n{sub e}{sup 2}}, has also been compared with α and DM. Additionally, we compare the distribution of α with the theoretically predicted model to infer the general characteristics of the ionized interstellar medium. Nearly 65% of the pulsars show a flatter index (i.e., α<4.4) than that expected from the Kolmogorov turbulence model. Moreover, the group of pulsars with flatter indices is typically associated with an enhanced value of C{sub n{sub e}{sup 2}} compared to those with steeper indices.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

Constraining Hydrological and Cryospheric Mass Flux in Southeastern Alaska using Space-Based Gravity Measurements

Watersheds draining into the Gulf of Alaska (GoA) experience large seasonal and inter-annual variations of water in the form of rain, snow, and ice, but accurate constraints on these variations have been difficult to obtain. Over larger geographic regions, water variations can be inferred directly from the Gravity Recovery and Climate Experiment (GRACE) data. However, because GoA variations occur over such a small region, the inferred average value of water flux increases as the applied smoothing of the GRACE data decreases. We use this observed scaling together with scaling results obtained from forward models to infer a seasonal amplitude of 115 plus or minus 20 cubic kilometers of water and an average contribution to sea level rise over the two years of data of 0.31 plus or minus 0.09 millimeters per year. These results suggest that accelerated melting that began in the late 1990s, as inferred from altimetry, continues unabated.

Tamisiea, M. E.↗
DOE OSTI2014

Current Trends in the Detection of Sociocultural Signatures: Data-Driven Models

The harvesting of behavioral data and their analysis through evidence-based reasoning enable the detection of sociocultural signatures in their context to support situation awareness and decision making. Harvested data are used as training materials from which to infer computational models of sociocultural behaviors or calibrate parameters for such models. Harvested data also serve as evidence input that the models use to provide insights about observed and future behaviors for targets of interest. The harvested data is often the result of assembling diverse data types and aggregating them into a form suitable for analysis. Data need to be analyzed to bring out the categories of content that are relevant to the domain being addressed in order to train or run a model. If, for example, we are modeling the intent of a group to engage in violent behavior using messages that the group has broadcasted, then these messages need to be processed to extract and measure indicators of violent intent. The extracted indicators and the associated measurements (e.g. rates or counts of occurrence) can then be used to train/calibrate and run computational models that assess the propensity for violence expressed in the source message. Ubiquitous access to the Internet, mobile telephony and technologies such as digital photography and digital video have enabled social media application platforms such as Facebook, YouTube, and Twitter that are altering the nature of human social interaction. The fast increasing pace of online social interaction introduces new challenges and opportunities for gathering sociocultural data. Challenges include the development of harvesting and processing techniques tailored to new data environments and formats (e.g. Twitter, Facebook), the integration of social media content with traditional media content, and the protection of personal privacy. As these and other challenges are addressed, a new wealth of behavioral data and data analysis methods becomes available that are shaping social computing as a strongly data-driven experimental discipline with an increasingly stronger impact on the decision-making process of groups and individuals alike. In this chapter, we review current advances and trends in the detection of sociocultural signatures. Specific embodiments of the issues discussed are provided with respect to the assessment of violent intent and sociopolitical contention. We begin by reviewing current approaches to the detection of sociocultural signatures in these domains. Next, we turn to the review of novel data harvesting methods for social media content. Finally, we discuss the application of sociocultural models to social media content, and conclude by commenting on current challenges and future developments.

Sanfilippo, Antonio P.↗
DOE OSTI2017

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

Morton, April↗
DOE OSTI2015

Electric field and temperature dependence of dielectric permittivity in strontium titanate investigated by a photoemission study on Pt/SrTiO{sub 3}:Nb junctions

Schottky junctions made from platinum and niobium-doped strontium titanate (SrTiO{sub 3}:Nb) were investigated by hard X-ray photoemission (HXPES) and through a band bending behavior simulation using a phenomenological model, which assumes a decrease in dielectric constant due to an electric field. Thus, we confirmed that the observed HXPES spectra at relatively high temperatures, e.g., >250 K, were well simulated using this phenomenological model. In contrast, it was inferred that the model was not appropriate for junction behavior at lower temperatures, e.g., <150 K. Therefore, a reconstruction of the phenomenological model is necessary to adequately explain the dielectric properties of SrTiO{sub 3}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Modeling neutrino-induced scale-dependent galaxy clustering for photometric galaxy surveys

Abstract The increasing statistical precision of photometric redshift surveys requires improved accuracy of theoretical predictions for large-scale structure observables to obtain unbiased cosmological constraints. In ΛCDM cosmologies, massive neutrinos stream freely at small cosmological scales, suppressing the small-scale power spectrum. In massive neutrino cosmologies, galaxy bias modeling needs to accurately relate the scale-dependent growth of the underlying matter field to observed galaxy clustering statistics. In this work, we implement a computationally efficient approximation of the neutrino-induced scale-dependent bias (NISDB). Through simulated likelihood analyses of Dark Energy Survey Year 3 (DESY3) and Legacy Survey of Space and Time Year 1 (LSSTY1) synthetic data that contain an appreciable NISDB, we examine the impact of linear galaxy bias and neutrino mass modeling choices on cosmological parameter inference. We find model misspecification of the NISDB approximation and neutrino mass models to decrease the constraining power of photometric galaxy surveys and cause parameter biases in the cosmological interpretation of future surveys. We quantify these biases and devise mitigation strategies.

Astronomy & Astrophysics↗
NASA NTRS2011

Verification of Small Hole Theory for Application to Wire Chaffing Resulting in Shield Faults

Our work is focused upon developing methods for wire chafe fault detection through the use of reflectometry to assess shield integrity. When shielded electrical aircraft wiring first begins to chafe typically the resulting evidence is small hole(s) in the shielding. We are focused upon developing algorithms and the signal processing necessary to first detect these small holes prior to incurring damage to the inner conductors. Our approach has been to develop a first principles physics model combined with probabilistic inference, and to verify this model with laboratory experiments as well as through simulation. Previously we have presented the electromagnetic small-hole theory and how it might be applied to coaxial cable. In this presentation, we present our efforts to verify this theoretical approach with high-fidelity electromagnetic simulations (COMSOL). Laboratory observations are used to parameterize the computationally efficient theoretical model with probabilistic inference resulting in quantification of hole size and location. Our efforts in characterizing faults in coaxial cable are subsequently leading to fault detection in shielded twisted pair as well as analysis of intermittent faulty connectors using similar techniques.

Schuet, Stefan R.↗