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NASA NTRSDate not supplied

Antarctic Meteorites: A Statistical Look at a Uniquely Valuable Resource

As of the 2019-20 field season, the U.S. Antarctic meteorite program has collected >23,000 meteorites. The U.S. collection is valuable in that it is classified in its entirety. The systematic collection methods employed have pro-vided meteorites of >40 types, many of which are the first of their type ever recognized. One of the early drivers for characterization of the entire U.S. Antarctic collection was to allow statistical comparisons. Early statistical assessments examined mass distributions and the relative frequency of meteorite types as well as comparisons to a defined set of modern falls. Using these statistics some have argued that the flux of H chondrites changed over time. Harvey used model size distributions to deconstruct the contribution of wind movement, meteorite supply and search losses to the Antarctic collection. Mass-based statistics and size distribution comparisons were examined, including comparison with modern falls/Saharan finds. Focus has been on geospatial statistics, with a comprehensive overview of the statistics of the Antarctic collections provided from the first 35 seasons of U.S. collection by ANSMET, as well as more recent assessments.

Antarctic meteorite↗
NASA NTRSDate not supplied

Statistical Influence

Advancing the spread and practice of statistics enhances an organization’s ability to successfully achieve their mission. While there may be leadership mandates to employ statistical methods, often the spread of statistical concepts flourishes more effectively through the practice of statistical influence. At first glance, the term influence may seem to imply a passive and unenthusiastic posture toward promoting organizational change. However, a classical definition states that ,“In a general sense, influence denotes power whose operation is invisible and known only by its effects, or a power whose cause and operation are unseen.” This definition articulates the powerful and yet subtle aspects of influence that embodies the theme of this presentation. Stated plainly, powerful statistical concepts become more widely known and engrained primarily through demonstrated impact; a strategy known only by its effects. In this presentation, elements of statistical influence are exemplified through practice at NASA.

Peter A. Parker↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Statistical Downscaling of Climate Models for Solar Resource Assessment

This study presents the development of statistical models to efficiently downscale future projections of solar irradiance for solar energy applications. A climate data set simulated from a Regional Climate Model (RCM) obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) is selected as input to the statistical models to create high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). Our approach builds statistical downscaling models that (1) regrid RCM data (0.22 degree and daily spatiotemporal resolution), (2) correct bias of GHI projections, (3) downscale the future GHI project from daily-scale to hourly-scale, and (4) spatially downscale to generate GHI at 8-km resolution. To calibrate and validate the statistical models, we adapt and use the National Solar Radiation Database (NSRDB). Preliminary results show that the statistical downscaling approach downscales future projections of GHI under two climate scenarios (RCP4.5 and RCP8.5) with a nBIAS of 3%, nMAE of 34% and nRMSE of 46% estimated against NSRDB for the contiguous United State. This presentation will summarize the implemented methodology and validation results as well as future extension of this research.

climate data↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantum statistical plasmonic metacrystals

Engineering materials that control quantum many-body dynamics remains challenging, as multiparticle interactions typically produce complex emergent behaviour that is difficult to predict. Here we introduce quantum statistical plasmonic metacrystals, structures in which the multiparticle dynamics mediated by optical near fields produce forbidden quantum statistical bands that enable selective transmission of different types of light. This functionality arises from a plasmonic structure composed of nanoantennas acting as meta-atoms. Multiphoton fields with statistics within the allowed bands propagate without distortion, whereas fields in forbidden bands are suppressed or driven towards the nearest accessible statistical state. We show that these bands are determined by the geometry and collective arrangement of the meta-atoms, providing a deterministic route to engineering quantum statistical transport. This platform establishes a room-temperature quantum material intrinsically sensitive to the quantum coherence of many-body photonic systems, enabling their robust manipulation and transport. Our results have implications for coherence-sensitive photonic materials for energy harvesting and scalable many-body quantum technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

On the Statistical Uncertainty of Monte Carlo-Calculated Scattering Sensitivities

Sensitivity coefficients calculated with Monte Carlo codes are widely used for nuclear data uncertainty quantification in the modeling and simulation of complex 3D reactor systems. This study systematically compares sensitivity coefficients and associated statistical uncertainties for the multiplication factor and fuel temperature reactivity across multiple Monte Carlo codes (SCALE/KENO, SCALE/Shift, MCNP, and Serpent) using simple models representing light-water reactors and advanced reactor concepts. For multiplication factor sensitivities, statistical uncertainties are generally acceptable, although scattering sensitivities show significantly larger statistical uncertainties than, for example, fission and capture reactions. Fuel temperature reactivity sensitivities show significantly larger statistical uncertainties across all reactions. Elastic scattering sensitivities are the most problematic: all Monte Carlo codes fail to resolve energy-dependent coefficients, and they produce dramatically different energy-collapsed values. Critically, the use of these sensitivity coefficients in nuclear data uncertainty propagation leads to reduced statistical uncertainties in individual uncertainty contributions. This can lead to the masking of unusable sensitivity coefficients and producing misleading uncertainty results. The findings of this study show that new or enhanced methods are needed to improve Monte Carlo elastic scattering sensitivity calculations. Additionally, this study shows the relevance of verifying sensitivity coefficients through direct perturbation calculations for individual nuclide reactions, instead of only for total cross sections as commonly done.

Bostelmann, Rike [ORNL] (ORCID:0000000165968088)↗
NASA NTRS1974

Statistical modeling of scintillation effects

Scintillation produces fluctuation of the complex envelope of a modulated signal. A useful way to characterize scintillation effects is to describe the signal statistics that result when a CW wave is transmitted through a random medium. Many theoretical treatments describe the signal statistics in a manner identical with the noise theory of Rice. These theories, however, predict Rice statistics only at a very great distance from the perturbing medium, and it has been suspected that generalization to permit the quadrature components of the scattered signal to be partially correlated Gaussian variates might better match the observed signal statistics. Recent tests of signals observed through three types of structured plasma have consistently confirmed this speculation and have revealed a surprising consistency in parameters describing the first-order signal statistics.

Fremouw, E. J.↗
NASA NTRS1976

Joint aircraft loading/structure response statistics of time to service crack initiation

A reliability analysis for predicting the statistical distribution of time to fatigue crack initiation for aircraft structures in service is presented. The present analysis utilizes the statistical data of the specimen fatigue tests, the full-scale structure tests, and the statistical dispersion of aircraft service loads. The statistical distribution of the time to fatigue crack initiation of the full-scale structure under laboratory loading spectrum is assumed to be Weibull. The service loads for gust turbulences are modeled as Poisson processes for transport-type aircraft, while the maneuver loads are modeled as compound Poisson processes for fighter and training aircraft. It is found that the statistical distribution of time to fatigue crack initiation for aircraft structures in service is not Weibull and that the prediction on the basis of the Weibull distribution is unconservative, in particular in the early service time.

Yang, J.-N.↗
NASA NTRS1982

Statistics of Delta v magnitude for a trajectory correction maneuver containing deterministic and random components

A number of interplanetary missions now being planned involve placing deterministic maneuvers along the flight path to alter the trajectory. Lee and Boain (1973) examined the statistics of trajectory correction maneuver (TCM) magnitude with no deterministic ('bias') component. The Delta v vector magnitude statistics were generated for several values of random Delta v standard deviations using expansions in terms of infinite hypergeometric series. The present investigation uses a different technique (Monte Carlo simulation) to generate Delta v magnitude statistics for a wider selection of random Delta v standard deviations and also extends the analysis to the case of nonzero deterministic Delta v's. These Delta v magnitude statistics are plotted parametrically. The plots are useful in assisting the analyst in quickly answering questions about the statistics of Delta v magnitude for single TCM's consisting of both a deterministic and a random component. The plots provide quick insight into the nature of the Delta v magnitude distribution for the TCM.

Bollman, W. E.↗
NASA NTRS1987

Statistical aspects of solar flares

A survey of the statistical properties of 850 H alpha solar flares during 1975 is presented. Comparison of the results found here with those reported elsewhere for different epochs is accomplished. Distributions of rise time, decay time, and duration are given, as are the mean, mode, median, and 90th percentile values. Proportions by selected groupings are also determined. For flares in general, mean values for rise time, decay time, and duration are 5.2 + or - 0.4 min, and 18.1 + or 1.1 min, respectively. Subflares, accounting for nearly 90 percent of the flares, had mean values lower than those found for flares of H alpha importance greater than 1, and the differences are statistically significant. Likewise, flares of bright and normal relative brightness have mean values of decay time and duration that are significantly longer than those computed for faint flares, and mass-motion related flares are significantly longer than non-mass-motion related flares. Seventy-three percent of the mass-motion related flares are categorized as being a two-ribbon flare and/or being accompanied by a high-speed dark filament. Slow rise time flares (rise time greater than 5 min) have a mean value for duration that is significantly longer than that computed for fast rise time flares, and long-lived duration flares (duration greater than 18 min) have a mean value for rise time that is significantly longer than that computed for short-lived duration flares, suggesting a positive linear relationship between rise time and duration for flares. Monthly occurrence rates for flares in general and by group are found to be linearly related in a positive sense to monthly sunspot number. Statistical testing reveals the association between sunspot number and numbers of flares to be significant at the 95 percent level of confidence, and the t statistic for slope is significant at greater than 99 percent level of confidence. Dependent upon the specific fit, between 58 percent and 94 percent of the variation can be accounted for with the linear fits. A statistically significant Northern Hemisphere flare excess (P less than 1 percent) was found, as was a Western Hemisphere excess (P approx 3 percent). Subflares were more prolific within 45 deg of central meridian (P less than 1 percent), while flares of H alpha importance or = 1 were more prolific near the limbs greater than 45 deg from central meridian; P approx 2 percent). Two-ribbon flares were more frequent within 45 deg of central meridian (P less than 1 percent). Slow rise time flares occurred more frequently in the western hemisphere (P approx 2 percent), as did short-lived duration flares (P approx 9 percent), but fast rise time flares were not preferentially distributed (in terms of east-west or limb-disk). Long-lived duration flares occurred more often within 45 deg 0 central meridian (P approx 7 percent). Mean durations for subflares and flares of H alpha importance or + 1, found within 45 deg of central meridian, are 14 percent and 70 percent, respectively, longer than those found for flares closer to the limb. As compared to flares occurring near cycle maximum, the flares of 1975 (near solar minimum) have mean values of rise time, decay time, and duration that are significantly shorter. A flare near solar maximum, on average, is about 1.6 times longer than one occurring near solar minimum.

Wilson, Robert M.↗
NASA NTRS1993

NASA thesaurus combined file postings statistics

The NASA Thesaurus Combined File Postings Statistics is published semiannually (January and July). This alphabetical listing of postable subject terms contained in the NASA Thesaurus is used to display the number of postings (documents) indexed by each subject term from 1968 to date. The postings totals per item are separated by announcement of other media into STAR, IAA, COSMIC, and OTHER, columnar entries covering the NASA document collection (1968 to date). This is a cumulative publication, and except for special cases, no reference is needed to previous issuances. Retention of the January 1992 issue could be helpful for book information. With the July 1992 issue, NALNET book statistics have been replaced by COSMIC statistics for NASA funded software. File postings statistics for the Alternate Data Base covering NASA collection from 1962 through 1967 were published on a one-time basis in September 1975. Subject terms for the Alternate Data Base are derived from the subject Authority List, reprinted 1985, which is available upon request. The distribution of 19,697,748 postings among the 17,446 NASA Thesaurus terms is tabulated on the last page of the NASA Thesaurus Combined File Postings Statistics.

Source record↗
NASA NTRS1992

Detector noise statistics in the non-linear regime

The statistical behavior of an idealized linear detector in the presence of threshold and saturation levels is examined. It is assumed that the noise is governed by the statistical fluctuations in the number of photons emitted by the source during an exposure. Since physical detectors cannot have infinite dynamic range, our model illustrates that all devices have non-linear regimes, particularly at high count rates. The primary effect is a decrease in the statistical variance about the mean signal due to a portion of the expected noise distribution being removed via clipping. Higher order statistical moments are also examined, in particular, skewness and kurtosis. In principle, the expected distortion in the detector noise characteristics can be calibrated using flatfield observations with count rates matched to the observations. For this purpose, some basic statistical methods that utilize Fourier analysis techniques are described.

Shopbell, P. L.↗
NASA NTRS1994

Comments on the statistical analysis of excess variance in the COBE differential microwave radiometer maps

Cosmic anisotrophy produces an excess variance sq sigma(sub sky) in the Delta maps produced by the Differential Microwave Radiometer (DMR) on cosmic background explorer (COBE) that is over and above the instrument noise. After smoothing to an effective resolution of 10 deg, this excess sigma(sub sky)(10 deg), provides an estimate for the amplitude of the primordial density perturbation power spectrum with a cosmic uncertainty of only 12%. We employ detailed Monte Carlo techniques to express the amplitude derived from this statistic in terms of the universal root mean square (rms) quadrupole amplitude, (Q sq/RMS)(exp 0.5). The effects of monopole and dipole subtraction and the non-Gaussian shape of the DMR beam cause the derived (Q sq/RMS)(exp 0.5) to be 5%-10% larger than would be derived using simplified analytic approximations. We also investigate the properties of two other map statistics: the actual quadrupole and the Boughn-Cottingham statistic. Both the sigma(sub sky)(10 deg) statistic and the Boughn-Cottingham statistic are consistent with the (Q sq/RMS)(exp 0.5) = 17 +/- 5 micro K reported by Smoot et al. (1992) and Wright et al. (1992).

Wright, E. L.↗
NASA NTRS1994

Statistical uncertainties in temperature diagnostics for hot coronal plasma using the ASCA SIS

Statistical uncertainties in determining the temperatures of hot (0.5-10 keV) coronal plasmas are investigated. The statistical presicion of various spectral temperature diagnostics is established by analyzing synthetic ASCA solid-state imaging spectrometer (SIS) CCD spectra. The diagnostics considered are the ratio of hydrogen-like to helium-like line complexes of Z greater than or = 14 elements, line-free portions of the continuum, and the entire spectrum. While fits to the entire spectrum yield the highest statistical precision, it is argued that fits to the line-free continuum are less susceptible to atomic data uncertainties but lead to a modest increase in statistical uncertainty over full spectral fits. Temperatures deduced from line ratios can have similar accuracy, but only over a narrow range of temperatures. Convenient estimates of statistical accuracies for the various temperature diagnostics are provided which may be used in planning ASCA SIS observations.

Swartz, Douglas A.↗
NASA NTRS1993

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

Starlinger, Alois↗
NASA NTRS2003

A Stochastic Model of Space-Time Variability of Mesoscale Rainfall: Statistics of Spatial Averages

A characteristic feature of rainfall statistics is that they depend on the space and time scales over which rain data are averaged. A previously developed spectral model of rain statistics that is designed to capture this property, predicts power law scaling behavior for the second moment statistics of area-averaged rain rate on the averaging length scale L as L right arrow 0. In the present work a more efficient method of estimating the model parameters is presented, and used to fit the model to the statistics of area-averaged rain rate derived from gridded radar precipitation data from TOGA COARE. Statistical properties of the data and the model predictions are compared over a wide range of averaging scales. An extension of the spectral model scaling relations to describe the dependence of the average fraction of grid boxes within an area containing nonzero rain (the "rainy area fraction") on the grid scale L is also explored.

Kundu, Prasun K.↗