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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

TRANSIT TIMING OBSERVATIONS FROM KEPLER. VI. POTENTIALLY INTERESTING CANDIDATE SYSTEMS FROM FOURIER-BASED STATISTICAL TESTS

We analyze the deviations of transit times from a linear ephemeris for the Kepler Objects of Interest (KOI) through quarter six of science data. We conduct two statistical tests for all KOIs and a related statistical test for all pairs of KOIs in multi-transiting systems. These tests identify several systems which show potentially interesting transit timing variations (TTVs). Strong TTV systems have been valuable for the confirmation of planets and their mass measurements. Many of the systems identified in this study should prove fruitful for detailed TTV studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Statistical Tests for Diagnosing Fission Source Convergence and Undersampling in Monte Carlo Criticality Calculations [Slides]

There is a very strong need for statistical testing to determine fission source convergence in Monte Carlo criticality calculations. Automation of such tests will greatly streamline and support the work carried out by NCS practitioners. Recent R&D work has shown that no single statistical test for convergence is sufficiently reliable, robust, and “guaranteed.” However, a combination of several standard statistical tests for the similarity of distributions, coupled with a high-fidelity estimate of the fission-matrix source is sufficiently robust, reliable, and repeatable that convergence can be “guaranteed.” During the course of the EG-AMCT studies, a number of statistical metrics and tests were proposed for diagnosing clustering and undersampling. None of these was robust and reliable enough for practical use in production codes. However, the expert group efforts came close. Some recent R&D work stemming from those past efforts has been very successful and promising. This Sub-Group will provide international input and collaboration on the development and implementation of statistical tests for convergence, with the primary goal of having the MC codes automatically detect convergence (or lack thereof). Newly proposed statistical tests to detect undersampling (after convergence) will also be reviewed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Statistical tests on clustered global earthquake synthetic data sets

Abstract We study the ability of statistical tests to identify nonrandom features of earthquake catalogs, with a focus on the global earthquake record since 1900. We construct four types of synthetic data sets containing varying strengths of clustering, with each data set containing on average 10,000 events over 100 years with magnitudes above M = 6. We apply a suite of statistical tests to each synthetic realization in order to evaluate the ability of each test to identify the sequences of events as nonrandom. Our results show that detection ability is dependent on the quantity of data, the nature of the type of clustering, and the specific signal used in the statistical test. Data sets that exhibit a stronger variation in the seismicity rate are generally easier to identify as nonrandom for a given background rate. We also show that we can address this problem in a Bayesian framework, with the clustered data sets as prior distributions. Using this new Bayesian approach, we can place quantitative bounds on the range of possible clustering strengths that are consistent with the global earthquake data. At M = 7, we can estimate 99th percentile confidence bounds on the number of triggered events, with an upper bound of 20% of the catalog for global aftershock sequences, with a stronger upper bound on the fraction of triggered events of 10% for long‐term event clusters. At M = 8, the bounds are less strict due to the reduced number of events. However, our analysis shows that other types of clustering could be present in the data that we are unable to detect. Our results aid in the interpretation of the results of statistical tests on earthquake catalogs, both worldwide and regionally.

Daub, Eric G.↗
DOE OSTI2013

Quantum Statistical Testing of a QRNG Algorithm

We present the algorithmic design of a quantum random number generator, the subsequent synthesis of a physical design and its verification using quantum statistical testing. We also describe how quantum statistical testing can be used to diagnose channel noise in QKD protocols.

quantum information↗
DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRAs of nuclear power plants (NPPs), and (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware, development of a philosophical basis for defining software failure, and identification of desirable attributes of quantitative software reliability methods. Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. This paper describes a statistical software testing approach for quantifying software reliability and applies it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

A Statistical Testing Approach for Quantifying Software Reliability; Application to an Example System

The U.S. Nuclear Regulatory Commission (NRC) encourages the use of probabilistic risk assessment (PRA) technology in all regulatory matters, to the extent supported by the state-of-the-art in PRA methods and data. Although much has been accomplished in the area of risk-informed regulation, risk assessment for digital systems has not been fully developed. The NRC established a plan for research on digital systems to identify and develop methods, analytical tools, and regulatory guidance for (1) including models of digital systems in the PRA's of nuclear power plants (NPPs), and, (2) incorporating digital systems in the NRC's risk-informed licensing and oversight activities. Under NRC's sponsorship, Brookhaven National Laboratory (BNL) explored approaches for addressing the failures of digital instrumentation and control (I and C) systems in the current NPP PRA framework. Specific areas investigated included PRA modeling digital hardware , development of a philosophical basis for defining software failure , and identification of desirable attributes of quantitative software reliability methods . Based on the earlier research, statistical testing is considered a promising method for quantifying software reliability. It is widely recognized that software failures are due to the triggering of pre-existing defects by the software's operational environment. Software defects can arise from errors made in user requirements or coding errors introduced during the developmental process. The software's operational environment includes factors such as the time history of digital system inputs, communication interfaces, the internal state of the digital system, and external conditions. Thus, software reliability is a function of both the number of pre-existing defects and the presence of a triggering condition caused by the manner in which the software is used. In this paper, we describe a statistical software-testing approach for quantifying software reliability and applied it to the loop-operating control system (LOCS) of an experimental loop of the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The work involved collaboration between BNL and INL. The objectives of the study include: (1) Development of a statistical testing approach for estimating software failure probability on demand, the results of which are suitable for including in a probabilistic risk assessment (PRA); and, (2) Application of the approach to the LOCS to estimate its failure probability, and obtain insights into the feasibility, practicality, and usefulness of the estimation in models of digital systems for inclusion in nuclear power plants' PRAs. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Statistical Tests for Convergence in Monte Carlo Criticality Calculations

This report describes methods for using statistical tests to automatically determine convergence of Monte Carlo (MC) iterations in criticality calculations. The methods described herein are intended to replace the traditional approach used for the past 60 years: make a trial run; determine convergence based on plots of k-effective and Shannon entropy vs cycle; adjust the input parameters for controlling the iterations; make a final run to obtain results and statistics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

Abstract. A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

To have value, comparisons of high-throughput phenotyping methods need statistical tests of bias and variance

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson’s correlation coefficient ( r ) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Quantum Statistical Testing of a Quantum Random Number Generator

The unobservable elements in a quantum technology, e.g., the quantum state, complicate system verification against promised behavior. Using model-based system engineering, we present methods for verifying the opera- tion of a prototypical quantum random number generator. We begin with the algorithmic design of the QRNG followed by the synthesis of its physical design requirements. We next discuss how quantum statistical testing can be used to verify device behavior as well as detect device bias. We conclude by highlighting how system design and verification methods must influence effort to certify future quantum technologies.

quantum communication↗
DOE OSTI2016

Statistical tests of galactic dynamo theory

Mean-field galactic dynamo theory is the leading theory to explain the prevalence of regular magnetic fields in spiral galaxies, but its systematic comparison with observations is still incomplete and fragmentary. Here we compare predictions of mean-field dynamo models to observational data on magnetic pitch angle and the strength of the mean magnetic field. We demonstrate that a standard α{sup 2}Ω dynamo model produces pitch angles of the regular magnetic fields of nearby galaxies that are reasonably consistent with available data. The dynamo estimates of the magnetic field strength are generally within a factor of a few of the observational values. Reasonable agreement between theoretical and observed pitch angles generally requires the turbulent correlation time τ to be in the range of 10–20 Myr, in agreement with standard estimates. Moreover, good agreement also requires that the ratio of the ionized gas scale height to root-mean-square turbulent velocity increases with radius. Our results thus widen the possibilities to constrain interstellar medium parameters using observations of magnetic fields. This work is a step toward systematic statistical tests of galactic dynamo theory. Such studies are becoming more and more feasible as larger data sets are acquired using current and up-and-coming instruments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis

In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam’s razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two methodologies by using the Bayes factor as a test statistic in the frequentist analysis. We show that the standard frequentist maximum likelihood-ratio test statistic corresponds to the Bayes factor with a non-informative Jeffrey’s prior. We also show that mixed priors increase the statistical power in frequentist analyses over the maximum likelihood test statistic. We develop an analytic formalism that does not require expensive simulations and generalize Wilks’ theorem beyond its usual regime of validity. In specific limits, the formalism reproduces existing expressions, such as the p-value of linear models and periodograms. We apply the formalism to an example of exoplanet transits, where multiplicity can be more than 10 7 . We show that our analytic expressions reproduce the $p$-values derived from numerical simulations. We offer an interpretation of our formalism based on the statistical mechanics. We introduce the counting of states in a continuous parameter space using the uncertainty volume as the quantum of the state. We show that both the $p$-value and Bayes factor can be expressed as an energy versus entropy competition.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Statistical tests of simple earthquake cycle models: Testing Earthquake Cycle Models

A central goal of observing and modeling the earthquake cycle is to forecast when a particular fault may generate an earthquake: a fault late in its earthquake cycle may be more likely to generate an earthquake than a fault early in its earthquake cycle. Models that can explain geodetic observations throughout the entire earthquake cycle may be required to gain a more complete understanding of relevant physics and phenomenology. Previous efforts to develop unified earthquake models for strike-slip faults have largely focused on explaining both preseismic and postseismic geodetic observations available across a few faults in California, Turkey, and Tibet. An alternative approach leverages the global distribution of geodetic and geologic slip rate estimates on strike-slip faults worldwide. Here we use the Kolmogorov-Smirnov test for similarity of distributions to infer, in a statistically rigorous manner, viscoelastic earthquake cycle models that are inconsistent with 15 sets of observations across major strike-slip faults. We reject a large subset of two-layer models incorporating Burgers rheologies at a significance level of α = 0.05 (those with long-term Maxwell viscosities η M <~ 4.0 × 10 19 Pa s and η M >~ 4.6 × 10 20 Pa s) but cannot reject models on the basis of transient Kelvin viscosity η K . Finally, we examine the implications of these results for the predicted earthquake cycle timing of the 15 faults considered and compare these predictions to the geologic and historical record.

58 GEOSCIENCES↗