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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Evaluating the design of satellite scanning radiometers for earth radiation budget measurements with system simulations. Part 1: Instantaneous estimates

A set of system simulations was performed to evaluate candidate scanner configurations to fly as a part of the Earth Radiation Budget Instrument (ERBI) on the polar platforms during the 1990's. The simulation is considered of instantaneous sampling (without diurnal averaging) of the longwave and shortwave fluxes at the top of the atmosphere (TOA). After measurement and subsequent inversion to the TOA, the measured fluxes were compared to the reference fluxes for 2.5 deg lat/long resolution targets. The reference fluxes at this resolution are obtained by integrating over the 25 x 25 = 625 grid elements in each target. The differences between each of these two resultant spatially averaged sets of target measurements (errors) are taken and then statistically summarized. Five instruments are considered: (1) the Conically Scanning Radiometer (CSR); (2) the ERBE Cross Track Scanner; (3) the Nimbus-7 Biaxial Scanner; (4) the Clouds and Earth's Radiant Energy System Instrument (CERES-1); and (5) the Active Cavity Array (ACA). Identical studies of instantaneous error were completed for many days, two seasons, and several satellite equator crossing longitudes. The longwave flux errors were found to have the same space and time characteristics as for the shortwave fluxes, but the errors are only about 25 pct. of the shortwave errors.

Stowe, Larry↗
DOE OSTI2026

Shuttling Majorana zero modes in disordered and noisy topological superconductors

The braiding of Majorana zero modes (MZMs) forms the fundamental building block for topological quantum computation. Braiding protocols which involve the physical exchange of MZMs are typically envisioned on a network of topological superconducting wires. An important component of these protocols is the transport of MZMs, which can be performed by using electric gates to locally tune sections of the wire between topologically trivial and nontrivial phases. In this work, we numerically simulate this transport by tuning a single section of a superconducting wire which contains either disorder (uncorrelated and correlated) or noise. We focus on the impact of these additional effects on the diabatic error, which describes unwanted transitions between the ground state and excited states. We show that the behavior of the average diabatic error is predominantly controlled by the statistics of the minimum bulk energy gap which is suppressed in the presence of disorder. The increase in diabatic error can be several orders of magnitude and is most deleterious when the disorder correlation length is a finite fraction of the transport distance and negligible when these lengths are far apart. In the presence of noise, the diabatic error is significantly enhanced due to optical transitions which depend on the minimum bulk energy gap as well as the frequency modes present in the noise. The results presented here serve to further characterize the diabatic error in disordered and noisy settings, which are important considerations in practical implementations of physical braiding schemes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

58 GEOSCIENCES↗
NASA NTRS2018

Combined Error and Uncertainty Estimates for CFD Problems

Given input sources of uncertainty, non-intrusive uncertainty propagation methods quantify the uncertainty in output quantities of interest (QoI) by performing a nite number of CFD (Computational Fluid Dynamics) instance realizations needed in the calculation of output statistics. It is well known that this introduces multiple sources of error. CFD codes often utilize finite-dimensional approximation (grids, basis functions, etc.) thus incurring CFD numerical errors often approximately reinterpreted as a statistical bias. Uncertainty propagation methods calculate uncertainty statistics for output quantities of interest using a numerical method (e.g. deterministic quadrature, sampling, etc.) thus incurring UQ (Uncertainty Quantification) numerical errors. Importance of quantifying these errors in large scale scientific computing: How accurate is an output statistic?; How should additional computational resources be invested to further reduce the error in a statistic?

Posteriori↗
NASA NTRS1988

Statistical corrections to the NMC medium range 700 mb height forecasts

This paper examines four statistical procedures for correcting the NMC medium-range forecast (MRF) errors in the 700-mb heights over the Northern Hemisphere. The tests were designed to include normal operational constraints, such as frequent changes in the forecast model and the lack of long-term forecast histories on which to base estimates of systematic errors and climate drifts. It was found that the two statistical procedures which provided a substantial reduction in mean square error were a procedure based on simple regression correction at each grid point and one based on lagged-average correction with one day lag at each grid point.

Schemm, Jae-Kyung E.↗
DOE OSTI2014

Quantitative analysis of the correlations in the Boltzmann-Grad limit for hard spheres

In this contribution I consider the problem of the validity of the Boltzmann equation for a system of hard spheres in the Boltzmann-Grad limit. I briefly review the results available nowadays with a particular emphasis on the celebrated Lanford’s validity theorem. Finally I present some recent results, obtained in collaboration with S. Simonella, concerning a quantitative analysis of the propagation of chaos. More precisely we introduce a quantity (the correlation error) measuring how close a j-particle rescaled correlation function at time t (sufficiently small) is far from the full statistical independence. Roughly speaking, a correlation error of order k, measures (in the context of the BBKGY hierarchy) the event in which k tagged particles form a recolliding group.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1974

Impact of scintillation of transionospheric communications

Analyses of AGC analog data from the Small Astronomy Satellite (SAS) at VHF (136 MHz) have shown amplitude fluctuations in the signals received at several equatorial tracking sites operated by NASA. Coincident digital telemetry data, transmitted from SAS at 1 kbps, has also been analyzed. Errors in data are shown to increase markedly for nighttime satellite passes around the periods of the two equinoxes as compared to the errors observed during other periods. This paper establishes the qualitative and quantitative correlations between the ionospheric scintillation phenomenon and the occurrence of errors. Strong time and geographic cause and effect relationships are established by determining the statistical correlation between scintillation indices and bit error rates.

Blank, H. A.↗
NASA NTRS1996

Global and Local Distortion Inference During Embedded Zerotree Wavelet Decompression

This paper presents algorithms for inferring global and spatially local estimates of the squared-error distortion measures for the Embedded Zerotree Wavelet (EZW) image compression algorithm. All distortion estimates are obtained at the decoder without significantly compromising EZW's rate-distortion performance. Two methods are given for propagating distortion estimates from the wavelet domain to the spatial domain, thus giving individual estimates of distortion for each pixel of the decompressed image. These local distortion estimates seem to provide only slight improvement in the statistical characterization of EZW compression error relative to the global measure, unless actual squared errors are propagated. However, they provide qualitative information about the asymptotic nature of the error that may be helpful in wavelet filter selection for low bit rate applications.

Huber, A. Kris↗
DOE OSTI2016

SU-F-T-362: Quantification and Modelling of the Ionization Chamber Simulation Effective Points On Monaco Treatment Planning System

Purpose: Because of statistical noise in Monte Carlo dose calculations, effective point doses may not be accurate. Volume spheres are useful for evaluating dose in Monte Carlo plans, which have an inherent statistical uncertainty.We use a user-defined sphere volume instead of a point, take sphere sampling around effective point make the dose statistics to decrease the stochastic errors. Methods: Direct dose measurements were made using a 0.125cc Semiflex ion chamber (IC) 31010 isocentrically placed in the center of a homogeneous Cylindric sliced RW3 phantom (PTW, Germany).In the scanned CT phantom series the sensitive volume length of the IC (6.5mm) were delineated and defined the isocenter as the simulation effective points. All beams were simulated in Monaco in accordance to the measured model. In our simulation using 2mm voxels calculation grid spacing and choose calculate dose to medium and request the relative standard deviation ≤0.5%. Taking three different assigned IC over densities (air electron density(ED) as 0.01g/cm3 default CT scanned ED and Esophageal lumen ED 0.21g/cm3) were tested at different sampling sphere radius (2.5, 2, 1.5 and 1 mm) statistics dose were compared with the measured does. Results: The results show that in the Monaco TPS for the IC using Esophageal lumen ED 0.21g/cm3 and sampling sphere radius 1.5mm the statistical value is the best accordance with the measured value, the absolute average percentage deviation is 0.49%. And when the IC using air electron density(ED) as 0.01g/cm3 and default CT scanned EDthe recommented statistical sampling sphere radius is 2.5mm, the percentage deviation are 0.61% and 0.70%, respectivly. Conclusion: In Monaco treatment planning system for the ionization chamber 31010 recommend air cavity using ED 0.21g/cm3 and sampling 1.5mm sphere volume instead of a point dose to decrease the stochastic errors. Funding Support No.C201505006.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TRUE MASSES OF RADIAL-VELOCITY EXOPLANETS

We study the task of estimating the true masses of known radial-velocity (RV) exoplanets by means of direct astrometry on coronagraphic images to measure the apparent separation between exoplanet and host star. Initially, we assume perfect knowledge of the RV orbital parameters and that all errors are due to photon statistics. We construct design reference missions for four missions currently under study at NASA: EXO-S and WFIRST-S, with external star shades for starlight suppression, EXO-C and WFIRST-C, with internal coronagraphs. These DRMs reveal extreme scheduling constraints due to the combination of solar and anti-solar pointing restrictions, photometric and obscurational completeness, image blurring due to orbital motion, and the “nodal effect,” which is the independence of apparent separation and inclination when the planet crosses the plane of the sky through the host star. Next, we address the issue of nonzero uncertainties in RV orbital parameters by investigating their impact on the observations of 21 single-planet systems. Except for two—GJ 676 A b and 16 Cyg B b, which are observable only by the star-shade missions—we find that current uncertainties in orbital parameters generally prevent accurate, unbiased estimation of true planetary mass. For the coronagraphs, WFIRST-C and EXO-C, the most likely number of good estimators of true mass is currently zero. For the star shades, EXO-S and WFIRST-S, the most likely numbers of good estimators are three and four, respectively, including GJ 676 A b and 16 Cyg B b. We expect that uncertain orbital elements currently undermine all potential programs of direct imaging and spectroscopy of RV exoplanets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel’dovich Survey

We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (z(median) = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z) M-500c/10(14)M(circle dot)) = A + 1.5ln (kT/7.2 keV) to A = 1.81(-0.14)(+0.24)(stat.)+/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c(200c) = 5.6(-1.8)(+3.7).

cosmology: observations↗
DOE OSTI2023

Seeing through noise in power laws

Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Seeing through noise in power laws

Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

The beam filling error in the Nimbus 5 electronically scanning microwave radiometer observations of Global Atlantic Tropical Experiment rainfall

A comparison of rain rates retrieved from the Nimbus 5 electronically scanning microwave radiometer brightness temperatures and observed from shipboard radars during the Global Atlantic Tropical Experiment (GATE) phase I shows that the beam filling error is the major source of discrepancy between the two. When averaged over a large scene (the GATE radar array, 400 km in diameter), the beam filling error is quite stable, being 50 percent of the observed rain rate. This suggests the simple procedure of multiplying retrieved rain rates by 2 (correction factor). A statistical model of the beam filling error is developed by envisioning an idealized instrument field-of-view that encompasses an entire gamma distribution of rain rates. A modeled correction factor near 2 is found for rain rate and temperature characteristics consistent with GATE conditions. The statistical model also suggests that the correction factor varies from 1.5 to 2.5 for suppressed to enhanced tropical convective regimes, and decreases to 1.5 as the freezing level and average depth of the rain column decreases to 2.5 km.

Short, David A.↗
NASA NTRS1990

Automatic recognition of intermittent failures - An experimental study of field data

A methodology is proposed for recognizing the symptoms of persistent problems in large systems. The system error rate is used to identify the error states among which relationships may exist. Statistical techniques are used to validate and quantify the strength of the relationship among these error states. As input, the approach takes the raw error logs containing a single entry for each error that is detected as an isolated event. As output, it produces a list of symptoms that characterize persistent errors. Thus, given a failure, it is determined whether the failure is an intermittent manifestation of a common fault or whether it is an isolated (transient) incident. The technique is shown to work on two CYBER systems and an IBM 3081 multiprocessor system. Comparisons to real failure/repair information obtained from field engineers show that, in about 85 percent of the cases, the error symptoms recognized by this approach correspond to real problems. The remaining 15 percent of the cases, although not directly supported by field data, are confirmed as being valid problems.

Iyer, Ravishankar K.↗
DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗
DOE OSTI2016

WE-DE-201-02: A Statistical Analysis Tool for Plan Quality Verification in HDR Brachytherapy Forward Planning for Cervix Cancer

Purpose: High Dose Rate (HDR) brachytherapy forward planning is principally an iterative process; hence, plan quality is affected by planners’ experiences and limited planning time. Thus, this may lead to sporadic errors and inconsistencies in planning. A statistical tool based on previous approved clinical treatment plans would help to maintain the consistency of planning quality and improve the efficiency of second checking. Methods: An independent dose calculation tool was developed from commercial software. Thirty-three previously approved cervical HDR plans with the same prescription dose (550cGy), applicator type, and treatment protocol were examined, and ICRU defined reference point doses (bladder, vaginal mucosa, rectum, and points A/B) along with dwell times were collected. Dose calculation tool then calculated appropriate range with a 95% confidence interval for each parameter obtained, which would be used as the benchmark for evaluation of those parameters in future HDR treatment plans. Model quality was verified using five randomly selected approved plans from the same dataset. Results: Dose variations appears to be larger at the reference point of bladder and mucosa as compared with rectum. Most reference point doses from verification plans fell between the predicted range, except the doses of two points of rectum and two points of reference position A (owing to rectal anatomical variations & clinical adjustment in prescription points, respectively). Similar results were obtained for tandem and ring dwell times despite relatively larger uncertainties. Conclusion: This statistical tool provides an insight into clinically acceptable range of cervical HDR plans, which could be useful in plan checking and identifying potential planning errors, thus improving the consistency of plan quality.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

In Flight Calibration of the Magnetospheric Multiscale Mission Fast Plasma Investigation

The Fast Plasma Investigation (FPI) on the Magnetospheric Multiscale mission (MMS) combines data from eight spectrometers, each with four deflection states, into a single map of the sky. Any systematic discontinuity, artifact, noise source, etc. present in this map may be incorrectly interpreted as legitimate data and incorrect conclusions reached. For this reason it is desirable to have all spectrometers return the same output for a given input, and for this output to be low in noise sources or other errors. While many missions use statistical analyses of data to calibrate instruments in flight, this process is difficult with FPI for two reasons: 1. Only a small fraction of high resolution data is downloaded to the ground due to bandwidth limitations and 2: The data that is downloaded is, by definition, scientifically interesting and therefore not ideal for calibration. FPI uses a suite of new tools to calibrate in flight. A new method for detection system ground calibration has been developed involving sweeping the detection threshold to fully define the pulse height distribution. This method has now been extended for use in flight as a means to calibrate MCP voltage and threshold (together forming the operating point) of the Dual Electron Spectrometers (DES) and Dual Ion Spectrometers (DIS). A method of comparing higher energy data (which has low fractional voltage error) to lower energy data (which has a higher fractional voltage error) will be used to calibrate the high voltage outputs. Finally, a comparison of pitch angle distributions will be used to find remaining discrepancies among sensors.

Detection-system↗