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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities

Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. Furthermore, this model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1998

On the Long-Term Trend of Atlantic Basin Intense Hurricanes

During the interval of 1944-1997, 120 intense hurricanes were observed in the Atlantic basin, having an annual frequency of 0-7 events per year, being more active prior to the mid 1960s than thereafter, and being preferentially lower during El Nino years as compared to non-El Nino years. Because decadal averages of the frequency of intense hurricanes closely resemble those of average temperature anomalies for northern hemispheric and global standards and of the average temperature at the Armagh Observatory (Northern Ireland), a proxy for climatic change, it is inferred that the long-term trends of the annual frequency of intense hurricanes and temperature are statistically related. Indeed, on the basis of the 4- and 10-yr moving averages, the two are found to be strongly associated (when temperature leads by 6 yr). Because the long-term leading trends of temperature are now upward, beginning about the mid 1980s, it is inferred that the long-term trends of the annual frequency of intense hurricanes should now also be upward, beginning near 1990, suggesting that a return to the more active state probably has already occurred.

Wilson, Robert M.↗
DOE OSTI2020

Bayesian Inference of the Symmetry Energy of Superdense Neutron-rich Matter from Future Radius Measurements of Massive Neutron Stars

In this work, using as references the posterior probability distribution functions of the equation of state (EOS) parameters inferred from the radii of canonical neutron stars (NSs) reported by the LIGO/VIRGO and NICER Collaborations based on their observations of GW170817 and PSR J0030+0451, we investigate how future radius measurements of more massive NSs will improve our current knowledge about the EOS of superdense neutron-rich nuclear matter, especially its symmetry energy term. Within the Bayesian statistical approach using an explicitly isospin-dependent parametric EOS for the core of NSs, we infer the EOS parameters of superdense neutron-rich nuclear matter from three sets of imagined mass–radius correlation data representing typical predictions by various nuclear many-body theories, that is, the radius stays the same, decreases, or increases with increasing NS mass within ±15% between 1.4 and 2.0 M$_\odot$. The corresponding NS average density increases quickly or slowly or slightly decreases as the NS mass increases from 1.4 to 2.0 M$_\odot$. While the EOSs of symmetric nuclear matter (SNM) inferred from the three data sets are approximately the same, the corresponding symmetry energies above about twice the saturation density of nuclear matter are very different, indicating that the radii of massive NSs carry important information about the high-density behavior of nuclear symmetry energy with little influence from the remaining uncertainties of the SNM EOS at suprasaturation densities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

FilDReaMS: II. Application to the analysis of the relative orientations between filaments and the magnetic field in four Herschel fields

Context. Both simulations and observations of the interstellar medium show that the study of the relative orientations between filamentary structures and the magnetic field can bring new insight into the role played by magnetic fields in the formation and evolution of filaments and in the process of star formation. Aims. We provide a first application of FilDReaMS, the new method presented in the companion paper to detect and analyze filaments in a given image. The method relies on a template that has the shape of a rectangular bar with variable width. Our goal is to investigate the relative orientations between the detected filaments and the magnetic field. Methods. We apply FilDReaMS to a small sample of four Herschel fields (G210, G300, G82, G202) characterized by different Galactic environments and different evolutionary stages. First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred from Planck polarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H2 column density. Results. We find preferential relative orientations in the four Herschel fields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H 2 column density ≃ 1.1 × 10 21 cm -2 and 1.4 × 10 21 cm -2 , respectively, consistent with the results of previous studies. Conclusions. Our results confirm the existence of a coupling between magnetic fields at cloud scales and filaments at smaller scale. They also illustrate the potential of combining Herschel and Planck observations, and they call for further statistical analyses with our dedicated method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Temporal Analysis Of Products Statistical Analysis Processing

Temporal analysis of products statistical analysis package (tapsap) is a package for reading, cleaning, analyzing and making inference of temporal analysis of products (TAP) information. This package is designed with the intent for collaboration within the TAP community for advancing TAP analysis.

Kunz, MatthewR↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Transient magnetic field signatures at high latitudes

We survey GOES 2/5/6 geosynchronous and Huancayo, Peru, ground magnetometer observations at the times of 70 transient (2-10 min) events recorded at South Pole Station, Antarctica. The simultaneous observations indicate that most South Pole events correspond to sudden sharp variations in the equatorial magnetospheric and low-latitude ground magnetic field. The exceptions occur when the South Pole events have weak amplitudes and/or Huancayo and GOES 2/5/6 are far from local noon. The corresponding features observed at GOES 5 and GOES 6 are generally similar, with a lag indicating antisunward motion. A similar antisunward motion may be inferred from the ground observations themselves. On a case-by-case and statistical basis, the characteristics of the events observed in South Pole ground magnetograms resemble those previously interpreted as sudden impulse and sudden storm commencement signatures at other high-latitude stations. These observations suggest that the transient events at South Pole form part of the magnetospheric and ionospheric response to a sudden change in the fraction of the solar wind dynamic pressure applied to the magnetosphere.

Sibeck, D. G.↗
NASA NTRS1994

High sensitivity dynamic spectral search for flare star radio

We observed ten well-known flare stars with the Arcibo radio telescope at 1.4 GHz and 5 GHz, using a special observing technique to discriminate between real flares and radio freqeuncy interference. With a high sensitivity of 5.5 K/Jy at 1.4 GHz when averaged over a 50 MHz band, we are able to recognize flux enhancements as weak as approximately 6 mJy above the sky background variations. In about 85 hours of observation, about a dozen bursts were detected, only from AD Leo. All had flux densities lower than 70 mJy, which probably explains their lack of fine structures (except for the strongest one), such as were reported in the literature for stronger flares. Half of the bursts that we recorded are 100% circularly polarized, and half are not circularly polarized. Our results are a first attempt of reliable statistics on dMe flare rates at 1.4 GHz. The high brightness temperatures we infer for the observed bursts are interpreted in terms of coherent emission processes, either the cyclotron maser instability or plasma radiation. Efficiencies are comparable to those of solar or planetary radio emissions in the case of the cyclotron maser, and higher than the solar efficiency in the case of plasma radiation, with the caveat that there are great uncertainties in the coronal model and the source size.

Abada-Simon, M.↗
DOE OSTI2026

Correlation-aware binning for small-angle neutron scattering via Gaussian-process inference

Binning in small-angle neutron scattering (SANS) is typically performed empirically, with fixed parameters chosen for convenience rather than statistical optimality. Such practices often fail to balance statistical precision and spatial resolution, leading to inconsistencies across instruments and datasets. Here we establish a correlation-aware framework that determines the optimal bin width from first principles by extending the classical Freedman–Diaconis (FD) rule to account for inter-bin correlations with a Gaussian process. In this formulation, the scattering intensity is treated as a smooth stochastic field whose statistical coherence is described by a covariance matrix. Analytical expressions of errors derived from this model yield closed-form criteria that separate the total deviation into contributions from counting noise, aliasing distortion and curvature-dependent correlation effects. Expressed in reduced variables, the resulting dimensionless error surface reveals a continuous transition from the uncorrelated FD regime to the correlation-dominated limit, providing a unified description of noise suppression and resolution control. Because the formulation depends only on the profile characteristics of scattering intensity I(Q), specifically its average intensity and first- and second-order derivatives, it applies generally to any SANS measurement regardless of sample, instrument or geometry. Experimental validation using small- and ultra-small-angle neutron scattering data confirms the predicted scaling behavior, demonstrating that correlation-aware inference systematically reduces mean-squared error and enables information-efficient reproducible data reduction across materials and instruments.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Understanding the fusion yield dependencies in OMEGA DT-layered implosion experiments using a physics-based statistical mapping model

Improving the performance of inertial confinement fusion implosions requires physics models that can accurately predict the response to changes in the experimental inputs. Good predictive capability has been demonstrated for the fusion yield using a statistical mapping of simulated outcomes to experimental data [Gopalaswamy et al., Nature 565(771), 581–586 (2019)]. In this paper, a physics-based statistical mapping approach is used to extract and quantify all the major sources of degradation of fusion yield for direct-drive implosions on the OMEGA laser. Here, the yield is found to be dependent on the age of the deuterium tritium fill, the ℓ = 1 asymmetry in the implosion core, the laser beam-to-target size ratio, and parameters related to the hydrodynamic stability. A controlled set of experiments were carried out where only the target fill age was varied while keeping all other parameters constant. The measurements were found to be in excellent agreement with the fill age dependency inferred using the mapping model. In addition, a new implosion design was created, guided by the statistical mapping model by optimizing the trade-offs between increased laser energy coupling at larger target size and the degradations caused by the laser beam-to-target size ratio and hydrodynamic instabilities. When experimentally performed, an increased fusion yield was demonstrated in targets with larger diameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1998

Deciphering the Long-Term Trend of Atlantic Basin Intense Hurricanes: More Active Versus Less Active During the Present Epoch

During the interval of 1944-1997, 120 intense hurricanes (i.e., those of category 3 or higher on the Saffir-Simpson hurricane damage potential scale) were observed in the Atlantic basin, having an annual frequency of 0-7 events per year, being more active prior to the mid 1960's than thereafter (hence a possible two-state division: more active versus less active), and being preferentially lower during El Nino years as compared to non-El Nino years. Because decadal averages of the frequency of intense hurricanes closely resemble those of average temperature anomalies for northern hemispheric and global standards and of the average temperature at the Armagh Observatory (Northern Ireland), a proxy for climatic change, it is inferred that the long-term trends of the annual frequency of intense hurricanes and temperature may be statistically related. Indeed, on the basis of 4- and 10-yr moving averages, one finds that there exists strong linear associations between the annual frequency of intense hurricanes in the Atlantic basin and temperature (specially, when temperature slightly leads). Because the long-term leading trends of temperature are now decidedly upward, beginning about the mid 1980's, it is inferred that the long-term consequential trends of the annual frequency of intense hurricanes should now also be upward, having begun near 1990, suggesting that a return to the more active state probably has already occurred. However, because of the anomalous El Nino activity of the early to mid 1990's, the switch from the less active to the more active state essentially went unnoticed (a marked increase in the number of intense hurricanes was not observed until the 1995 and 1996 hurricane seasons, following the end of the anomalous El Nino activity). Presuming that a return to the more active state has, indeed, occurred, one expects the number of seasonal intense hurricanes during the present epoch (continuing through about 2012) to usually be higher than average (i.e., greater than or equal to 2), except during El Nino-related seasons when the number usually will be less than average.

Wilson, Robert M.↗
DOE OSTI2022

Kronecker-structured covariance models for multiway data

Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bayesian event categorization matrix approach for explosion monitoring

Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.

58 GEOSCIENCES↗
DOE OSTI2020

Reconstructing gravity on cosmological scales

We present the data-driven late-time reconstruction of gravitational theories and dark energy models on cosmological scales. We showcase the power of present cosmological probes at constraining these models and quantify the knowledge of their properties that can be acquired through state of the art data. This reconstruction exploits the power of the effective field theory approach to dark energy and modified gravity phenomenology, which compresses the freedom in defining such models into a finite set of functions that can be reconstructed across cosmic times using cosmological data. We consider several model classes described within this framework and thoroughly discuss their phenomenology and data implications. In this work, we find that some models can alleviate the present discrepancy in the determination of the Hubble constant as inferred from the cosmic microwave background and as directly measured. This results in a statistically significant preference for the reconstructed theories over the standard cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

M-071 critical data analysis

A prototype balance study was conducted on earth prior to the balance studies conducted in Skylab itself. Collected were daily dietary intake data of 6 minerals and nitrogen, and fecal and urinary outputs on each of three astronauts. Essential statistical issues show what quantities need to be estimated and establish the scope of inference associated with alternative variance estimates. The procedures for obtaining the final variability due both to errors of measurement and total error (total = measurement and biological variability) are exhibited.

Hegsted, D. M.↗