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DOE OSTI2025

Cross-scale covariance for material property prediction

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~10 8 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10 2 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.

36 MATERIALS SCIENCE↗

NASA NTRS1978

Statistical considerations in design of spacelab experiments

After making an analysis of experimental error sources, statistical models were developed for the design and analysis of potential Space Shuttle experiments. Guidelines for statistical significance and/or confidence limits of expected results were also included. The models were then tested out on the following proposed Space Shuttle biomedical experiments: (1) bone density by computer tomography; (2) basal metabolism; and (3) total body water. Analysis of those results and therefore of the models proved inconclusive due to the lack of previous research data and statistical values. However, the models were seen as possible guides to making some predictions and decisions.

Robinson, J.↗

DOE OSTI2015

Five groups of red giants with distinct chemical composition in the globular cluster NGC 2808

The chemical composition of multiple populations in the massive globular cluster (GC) NGC 2808 is addressed with the homogeneous abundance reanalysis of 140 red giant branch stars. UVES spectra for 31 stars and GIRAFFE spectra for the other giants were analyzed with the same procedures used for about 2500 giants in 23 GCs in our FLAMES survey, deriving abundances of Fe, O, Na, Mg, Si, Ca, Ti, Sc, Cr, Mn, and Ni. Iron, elements from α capture, and those in the Fe group do not show intrinsic scatter. On our UVES scale, the metallicity of NGC 2808 is [Fe/H] = −1.129±0.005±0.034 (± statistical ± systematic error) with σ = 0.030 (31 stars). The main features related to proton-capture elements are retrieved, but the improved statistics and the smaller associated internal errors allow us to uncover five distinct groups of stars along the Na–O anticorrelation. We observe large depletions in Mg, anticorrelated with enhancements of Na and also Si, suggestive of unusually high temperatures for proton captures. About 14% of our sample is formed by giants with solar or subsolar [Mg/Fe] ratios. Using the [Na/Mg] ratios, we confirm the presence of five populations with different chemical compositions that we call P1, P2, I1, I2, and E in order of decreasing Mg and increasing Na abundances. Statistical tests show that the mean ratios in any pair of groups cannot be extracted from the same parent distribution. The overlap with the five populations recently detected from UV photometry is good but not perfect, confirming that more distinct components probably exist in this complex GC.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Connected and leading disconnected hadronic light-by-light contribution to the muon anomalous magnetic moment with a physical pion mass

We report a lattice QCD calculation of the hadronic light-by-light contribution to the muon anomalous magnetic moment at a physical pion mass. The calculation includes the connected diagrams and the leading, quark-line-disconnected diagrams. We incorporate algorithmic improvements developed in our previous work. The calculation was performed on the 48 3 × 96 ensemble generated with a physical pion mass and a 5.5 fm spatial extent by the RBC and UKQCD Collaborations using the chiral, domain wall fermion formulation. We find a HLbL μ = 5.35(1.35) × 10 –10 , where the error is statistical only. The finite-volume and finite lattice-spacing errors could be quite large and are the subject of ongoing research. Finally, the omitted disconnected graphs, while expected to give a correction of order 10%, also need to be computed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Understanding the Interplay between Hardware Errors and User Job Characteristics on the Titan Supercomputer

Designing dependable supercomputers begins with an understanding of errors in real-world, large-scale systems. The Titan supercomputer at Oak Ridge National Laboratory provides a unique opportunity to investigate errors when an actual system is actively used by multiple concurrent users and workloads from diverse domains at varying scales. This study presents a thorough analysis of 6, 908, 497 hardware errors from 18, 688 compute nodes of Titan for 312, 215 user jobs over a 3-year time period. Through careful joining of two system logs – the Machine Check Architecture (MCA) log and the job scheduler log – we show the correlated pattern of hardware errors for each job and user, in addition to individual descriptive statistics of errors, jobs, and users. Since the majority of hardware errors are memory errors, this study also shows the importance of error correcting in memory systems.

Lim, Seung-Hwan↗

NASA NTRS1990

The spin vector of Venus

Analysis of radar observations from 1964 through 1983 yields the following values for the components of the spin vector of Venus: P = 243.026 + or - 0.006 d (retrograde); alpha = 272.75 + or - 0.09 deg; and delta = 67.10 + or - 0.09 deg, where the standard errors quoted are three- to five-fold larger than the statistical standard errors and encompass the changes in results obtained by various tests designed to expose possible systematic errors. These values demonstrate conclusively that the spin state of Venus is not in resonance with the relative orbital motions of Venus and earth.

Shapiro, I. I.↗

DOE OSTI2026

Enhancing ZFP: A Statistical Approach to Understanding and Reducing Error Bias in a Lossy Floating-Point Compression Algorithm

The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Influence of ultraviolet irradiation on data retention characteristics in resistive random access memory

With increasing density of memory devices, the issue of generating soft errors by cosmic rays is becoming more and more serious. Therefore, the irradiation resistance of resistance random access memory (ReRAM) to cosmic radiation has to be elucidated for practical use. In this paper, we investigated the data retention characteristics of ReRAM against ultraviolet irradiation with a Pt/NiO/ITO structure. Soft errors were confirmed to be caused by ultraviolet irradiation in both low- and high-resistance states. An analysis of the wavelength dependence of light irradiation on data retention characteristics suggested that electronic excitation from the valence to the conduction band and to the energy level generated due to the introduction of oxygen vacancies caused the errors. Based on a statistically estimated soft error rates, the errors were suggested to be caused by the cohesion and dispersion of oxygen vacancies owing to the generation of electron-hole pairs and valence changes by the ultraviolet irradiation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2000

Evaluation of the Applicability of Solar and Lamp Radiometric Calibrations of a Precision Sun Photometer Operating Between 300 and 1025 nm

Over a period of 3 year a precision Sun photometer (SPM) operating between 300 and 1025 nm was calibrated four times at three different high-mountain sites in Switzerland, Germany, and the United States by means of the Langley-plot technique. We found that for atmospheric window wavelengths the total error (2 sigma-statistical plus systematic errors) of the calibration constants V(sub 0)(lambda), the SPM voltage in the absence of any attenuating atmosphere, can be kept below 1.60% in the UV-A and blue, 0.9% in the mid-visible, and 0.6% in the near-infra red spectral region. For SPM channels within strong water-vapor or ozone absorption bands a modified Langley-plot technique was used to determine V(sub 0)(lambda) with a lower accuracy. Within the same period of time, we calibrated the SPM five times using irradiance standard lamps in the optical labs of the Physikalisch-Meteorologisches Observatorium Davos and World Radiation Center, Switzerland, and of the Remote Sensing Group of the Optical Sciences Center, University of Arizona, Tucson, Arizona. The lab calibration method requires knowledge of the extraterrestrial spectral irradiance. When we refer the standard lamp results to the World Radiation Center extraterrestrial solar irradiance spectrum, they agree with the Langley results within 2% at 6 or 13 SPM wavelengths. The largest disagreement (4.4%) is found for the channel centered at 610 nm. The results of these intercomparisons change significantly when the lamp results are referred to two different extraterrestrial solar irradiance spectra that have become recently available.

Schmid, Beat↗

NASA NTRS2003

Estimation of Model Error Variances During Data Assimilation

Data assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data assimilation system.

Dee, Dick↗

DOE OSTI2016

MO-FG-CAMPUS-JeP3-01: A Statistical Model for Analyzing the Rotational Error of Single Iso-Center Technique

Purpose: To develop a generalized statistical model that incorporates the treatment uncertainty from the rotational error of single iso-center technique, and calculate the additional PTV (planning target volume) margin required to compensate for this error. Methods: The random vectors for setup and additional rotation errors in the three-dimensional (3D) patient coordinate system were assumed to follow the 3D independent normal distribution with zero mean, and standard deviations σx, σy, σz, for setup error and a uniform σR for rotational error. Both random vectors were summed, normalized and transformed to the spherical coordinates to derive the chi distribution with 3 degrees of freedom for the radical distance ρ. PTV margin was determined using the critical value of this distribution for 0.05 significant level so that 95% of the time the treatment target would be covered by ρ. The additional PTV margin required to compensate for the rotational error was calculated as a function of σx, σy, σz and σR. Results: The effect of the rotational error is more pronounced for treatments that requires high accuracy/precision like stereotactic radiosurgery (SRS) or stereotactic body radiotherapy (SBRT). With a uniform 2mm PTV margin (or σx =σy=σz=0.7mm), a σR=0.32mm will decrease the PTV coverage from 95% to 90% of the time, or an additional 0.2mm PTV margin is needed to prevent this loss of coverage. If we choose 0.2 mm as the threshold, any σR>0.3mm will lead to an additional PTV margin that cannot be ignored, and the maximal σR that can be ignored is 0.0064 rad (or 0.37°) for iso-to-target distance=5cm, or 0.0032 rad (or 0.18°) for iso-to-target distance=10cm. Conclusions: The rotational error cannot be ignored for high-accuracy/-precision treatments like SRS/SBRT, particularly when the distance between the iso-center and target is large.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Determination of errors in derived magnetic field directions in geosynchronous orbit: results from a statistical approach

This study aims to statistically estimate the errors in local magnetic field directions that are derived from electron directional distributions measured by Los Alamos National Laboratory geosynchronous (LANL GEO) satellites. First, by comparing derived and measured magnetic field directions along the GEO orbit to those calculated from three selected empirical global magnetic field models (including a static Olson and Pfitzer 1977 quiet magnetic field model, a simple dynamic Tsyganenko 1989 model, and a sophisticated dynamic Tsyganenko 2001 storm model), it is shown that the errors in both derived and modeled directions are at least comparable. Second, using a newly developed proxy method as well as comparing results from empirical models, we are able to provide for the first time circumstantial evidence showing that derived magnetic field directions should statistically match the real magnetic directions better, with averaged errors< ~2°, than those from the three empirical models with averaged errors >~5°. In addition, our results suggest that the errors in derived magnetic field directions do not depend much on magnetospheric activity, in contrast to the empirical field models. Finally, as applications of the above conclusions, we show examples of electron pitch angle distributions observed by LANL GEO and also take the derived magnetic field directions as the real ones so as to test the performance of empirical field models along the GEO orbits, with results suggesting dependence on solar cycles as well as satellite locations. This study demonstrates the validity and value of the method that infers local magnetic field directions from particle spin-resolved distributions.

58 GEOSCIENCES↗

NASA NTRS1971

Navigation analysis for Viking 1979, option B

A parametric study performed for 48 trans-Mars reference missions in support of the Viking program is reported. The launch dates cover several months in the year 1979, and each launch date has multiple arrival dates in 1980. A plot of launch versus arrival dates with case numbers designated for reference purposes is included. The analysis consists of the computation of statistical covariance matrices based on certain assumptions about the ground-based tracking systems. The error model statistics are listed in tables. Tracking systems were assumed at three sites: Goldstone, California; Canberra, Australia; and Madrid, Spain. The tracking data consisted of range and Doppler measurements taken during the tracking intervals starting at E-30(d) and ending at E-10(d) for the control data and ending at E-18(h) for the knowledge data. The control and knowledge covariance matrices were delivered to the Planetary Mission Analysis Branch for inputs into a delta V dispersion analysis.

Mitchell, P. H.↗

DOE OSTI2021

A machine learning approach for efficient multi-dimensional integration

Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is unbiased and has a statistically correct error estimation. Three ML models of multi-layer perceptron, gradient boosting decision tree, and Gaussian process regression algorithms are investigated. The performance of the proposed algorithm is demonstrated on six different families of integrands that typically appear in physics problems at various dimensions and integrand difficulties. The results show that, for the same total number of integrand evaluations, the new algorithm provides integral estimates with more than an order of magnitude smaller uncertainties than those of the VEGAS algorithm in most of the test cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Bootstrap estimation of the effect of instrument response function uncertainty on the reconstruction of fusion neutron sources

Neutron imagers are important diagnostics for the inertial confinement fusion implosions at the National Ignition Facility. They provide two- and three-dimensional reconstructions of the neutron source shape that are key indicators of the overall performance. To interpret the shape results properly, it is critical to estimate the uncertainty in those reconstructions. There are two main sources of uncertainties: limited neutron statistics, leading to random errors in the reconstructed images, and incomplete knowledge of the instrument response function (the pinhole-dependent point spread function). While the statistical errors dominate the uncertainty for lower yield deuterium-tritium (DT) shots, errors due to the instrument response function dominate the uncertainty for DT yields on the order of 10 16 neutrons or higher. In this work, a bootstrapping method estimates the uncertainty in a reconstructed image due to the incomplete knowledge of the instrument response function. The main reconstruction is created from the fixed collection of pinhole images that are best aligned with the neutron source. Additional reconstructions are then built using subsets of that collection of images. Variations in the shapes of these additional reconstructions originate solely from uncertainties in the instrument response function, allowing us to use them to provide an additional systematic uncertainty estimate.

47 OTHER INSTRUMENTATION↗

NASA NTRS1995

Model studies of the beam-filling error for rain-rate retrieval with microwave radiometers

Low-frequency (less than 20 GHz) single-channel microwave retrievals of rain rate encounter the problem of beam-filling error. This error stems from the fact that the relationship between microwave brightness temperature and rain rate is nonlinear, coupled with the fact that the field of view is large or comparable to important scales of variability of the rain field. This means that one may not simply insert the area average of the brightness temperature into the formula for rain rate without incurring both bias and random error. The statistical heterogeneity of the rain-rate field in the footprint of the instrument is key to determining the nature of these errors. This paper makes use of a series of random rain-rate fields to study the size of the bias and random error associated with beam filling. A number of examples are analyzed in detail: the binomially distributed field, the gamma, the Gaussian, the mixed gamma, the lognormal, and the mixed lognormal ('mixed' here means there is a finite probability of no rain rate at a point of space-time). Of particular interest are the applicability of a simple error formula due to Chiu and collaborators and a formula that might hold in the large field of view limit. It is found that the simple formula holds for Gaussian rain-rate fields but begins to fail for highly skewed fields such as the mixed lognormal. While not conclusively demonstrated here, it is suggested that the notionof climatologically adjusting the retrievals to remove the beam-filling bias is a reasonable proposition.

Ha, Eunho↗

NASA NTRS1976

LACIE performance predictor final operational capability program description, volume 1

The program EPHEMS computes the orbital parameters for up to two vehicles orbiting the earth for up to 549 days. The data represents a continuous swath about the earth, producing tables which can be used to determine when and if certain land segments will be covered. The program GRID processes NASA's climatology tape to obtain the weather indices along with associated latitudes and longitudes. The program LUMP takes substrata historical data and sample segment ID, crop window, crop window error and statistical data, checks for valid input parameters and generates the segment ID file, crop window file and the substrata historical file. Finally, the System Error Executive (SEE) Program checks YES error and truth data, CAMS error data, and signature extension data for validity and missing elements. A message is printed for each error found.

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