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The two tests employed in the hybrid testing scheme are Page’s cumulative sums for all streams within a Balance Period (maximum of the maximums and average of the maximums) and Crosier’s multivariate cumulative sum applied to incremental cumulative sums across Balance Periods. The role of residuals for both kinds of data is discussed.
Abstract not provided.
The Community Earth System Model Ensemble Consistency Test (CESM-ECT) suitewas developed as an alternative to requiring bit wise identical output for quality assurance. This objective test provides a statistical measurement of consistency between an accepted ensemble created by small initial temperature perturbations and a test set of CESM simulations. Here, we extend the CESM-ECT suite with an inexpensive and robust test for ensemble consistency that is applied to Community Atmospheric Model (CAM) output after only nine model time steps. We suggest that adequate ensemble variability is achieved with instantaneous variable values at the ninth step, despite rapid perturbation growth and heterogeneous variable spread. We refer to this new test as the Ultra-Fast CAM Ensemble Consistency Test (UF-CAM-ECT) and demonstrate its effectiveness in practice, including its ability to detect small-scale events and its applicability to the Community Land Model (CLM).The new ultra-fast test facilitates CESM development, porting, and optimization efforts, particularly when used to complement information fromthe original CESM-ECT suite of tools.
The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.
Here we develop a statistical basis for limited adverse testing. This type of testing simultaneously evaluates system performances against minimum requirements and minimizes costs, particularly for large scale engineering projects. Because testing is often expensive and narrow in scope, the data obtained is relatively limited—precisely the opposite of the recent “large data” movement but no less compelling. Although a remarkably common approach for industrial and large-scale government projects, a statistical basis for adverse testing remains poorly explored. Here we prove mathematically, under specific conditions, that setting each independent variable to an adverse condition leads to a similar level of adversity in the dependent variable. For example, setting all normally distributed independent variables to at least their 95th percentile values leads to a result at the 95th percentile. The analysis considers sample size estimates to clarify the value of replicates in this type of testing, determines how many of the independent variables must be set to adverse condition values, and highlights the essential assumptions, so that engineers, statisticians, and subject matter experts know when this statistical framework may be applied successfully.
We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance. The efficacy of the test statistic is shown via two examples of CP asymmetric distributions with varying sample sizes: the Dalitz distributions of B 0 → K + π – π 0 and of D 0 → π + π – π 0 decays. The windowed version of the Wasserstein distance test statistic is shown to have comparable sensitivity to CP violation as the commonly used energy test statistic, but also retains information about the localized distributions of CP asymmetry over the Dalitz plot. For large statistic datasets we introduce two modified Wasserstein distance based test statistics — the binned and the sliced Wasserstein distance statistics, which show comparable sensitivity to CP violation, but improved computing time and memory scalings. Finally, general extensions and applications of the introduced statistics are discussed.
Sandia National Laboratories develops and maintains several open-source software packages to support material accountancy analyses. This includes the Material Accountancy Performance Indicator Toolkit (MAPIT), the Fissile Facility Flow Modeler (F3M) and the Separation and Safeguards Performance Model Library (SSPM-L). MAPIT is responsible for performing statistical safeguards analyses on bulk and itemized data from nuclear fuel cycle facilities and can operate on real or synthetic data. MAPIT is the only open-source software for such analyses. F3M is a library of modules, built in MATLAB Simulink, that contain pre made blocks to represent different generic fuel cycle processes. These blocks can be used together in a modular fashion to represent and simulate nuclear fuel cycle processes with the goal of improving facility-level accountancy during the design phase. F3M is also an open-source library. Finally, the SSPM-L library is a series of completed models built from F3M. The library includes facility models such as a generic PUREX facility and a fuel fabrication facility. The SSPM-L library is not open source, but is available to collaborators with a relevant use case. These tools include modeling and simulation pipelines to simulate nuclear fuel cycle facilities and the underlying software needed to simulate measurement uncertainty and perform statistical analyses. Together, these tools can perform end-to-end nuclear material accountancy analyses. This report documents the various improvements made to these tools in FY25. Specifically, we added new statistical test, new statistical modeling capabilities, new fuel cycle facility models, and launched a new open-source model component library.
Disclosed are methods and systems for adjusting a High Voltage Direct Current (HVDC) compensator output signal to an HVDC system to reduce control loop modulus due to excessive time delay of sensor measurements. Two different time windows, a Short-Time Variance Window (STVW) and an Extended-Time Variance Window (ETVW) may be examined for detection of increased energy. To detect increased energy the HVDC compensator output signal may be passed through a bandpass filter, then variances for the STVW and ETVW may be generated to produce corresponding test statistics. The STVW and ETVW test statistics may be compared to hypothesis test thresholds and have binary logic based on the hypothesis test comparisons used to generate a gain. The gain may then be multiplied times the original HVDC feedback compensator output such that the adjusted HVDC feedback compensator output reduces the control loop modulus when applied to the HVDC system.
Algorithms for detecting illicit gamma radiation sources must respond to a wide variety of threat signatures and ignore a wider variety of clutter. Previously, we described the Nuisance-rejection Spectral Comparison Ratio Anomaly Detection (N-SCRAD) method for indicating the presence of threat sources during mobile search operations. Our method relies on the formulation of radiation alarm test statistics applied to detector counts in numerous spectral regions of interest and our test statistics are standardized by local estimates of background variability. In this work we present a new automated procedure for selecting test statistics and specifying the regions of interest based on minimizing detectable count rates of prototype threat spectra at a specified false positive rate using simulated annealing. The new procedure can be applied to configuring the detection algorithm when the prototype threats are many and diverse without significantly compromising the detection sensitivity to any one of them. N-SCRAD makes use of estimates of background variability obtained during a search from prior measurements of background. In applications such estimates made purely from past measurements sometimes do not extrapolate well to current conditions. In this work we present a new method for correcting those estimates to current conditions of the total count rate. We demonstrate that the correction lowers detection thresholds for a specified false positive rate, enabling greater sensitivity to targets. The two proposed improvements to N-SCRAD maximize detection power for the expected variety of possible threats while reducing or maintaining the false positive rate.
Systems, methods, and apparatus are disclosed herein for data pattern detection and data reduction. Devices include an input port configured to receive data values that include a plurality of data units. The devices may also include a data analyzer configured to determine a test statistic based on at least some of the plurality of data units, the test statistic indicating a degree of difference between a first data unit and at least a second data unit, the second data unit being received at the input port before the first data unit. The data analyzer includes the first data unit in a first data block responsive to a determination that the test statistic indicates a low degree of difference, the determination being based on a comparison with a designated difference threshold, the first data block being a same data block as a second data block that includes the second data unit.
Quantum state tomography on a d-dimensional system demands resources that grow rapidly with d. They may be reduced by using model selection to tailor the number of parameters in the model (i.e., the size of the density matrix). Most model selection methods typically rely on a test statistic and a null theory that describes its behavior when two models are equally good. Here, we consider the loglikelihood ratio. Because of the positivity constraint ρ ≥ 0, quantum state space does not generally satisfy local asymptotic normality (LAN), meaning the classical null theory for the loglikelihood ratio (the Wilks theorem) should not be used. Thus, understanding and quantifying how positivity affects the null behavior of this test statistic is necessary for its use in model selection for state tomography. We define a new generalization of LAN, metric-projected LAN, show that quantum state space satisfies it, and derive a replacement for the Wilks theorem. In addition to enabling reliable model selection, our results shed more light on the qualitative effects of the positivity constraint on state tomography.
Purpose: High-quality radiation therapy using highly conformal dose distributions and image-guided techniques requires optimum machine delivery performance. In this work, a monitoring system for multileaf collimator (MLC) performance, integrating semiautomated MLC quality control (QC) tests and statistical process control tools, was developed. The MLC performance monitoring system was used for almost a year on two commercially available MLC models. Control charts were used to establish MLC performance and assess test frequency required to achieve a given level of performance. MLC-related interlocks and servicing events were recorded during the monitoring period and were investigated as indicators of MLC performance variations. Methods: The QC test developed as part of the MLC performance monitoring system uses 2D megavoltage images (acquired using an electronic portal imaging device) of 23 fields to determine the location of the leaves with respect to the radiation isocenter. The precision of the MLC performance monitoring QC test and the MLC itself was assessed by detecting the MLC leaf positions on 127 megavoltage images of a static field. After initial calibration, the MLC performance monitoring QC test was performed 3–4 times/week over a period of 10–11 months to monitor positional accuracy of individual leaves for two different MLC models. Analysis of test results was performed using individuals control charts per leaf with control limits computed based on the measurements as well as two sets of specifications of ±0.5 and ±1 mm. Out-of-specification and out-of-control leaves were automatically flagged by the monitoring system and reviewed monthly by physicists. MLC-related interlocks reported by the linear accelerator and servicing events were recorded to help identify potential causes of nonrandom MLC leaf positioning variations. Results: The precision of the MLC performance monitoring QC test and the MLC itself was within ±0.22 mm for most MLC leaves and the majority of the apparent leaf motion was attributed to beam spot displacements between irradiations. The MLC QC test was performed 193 and 162 times over the monitoring period for the studied units and recalibration had to be repeated up to three times on one of these units. For both units, rate of MLC interlocks was moderately associated with MLC servicing events. The strongest association with the MLC performance was observed between the MLC servicing events and the total number of out-of-control leaves. The average elapsed time for which the number of out-of-specification or out-of-control leaves was within a given performance threshold was computed and used to assess adequacy of MLC test frequency. Conclusions: A MLC performance monitoring system has been developed and implemented to acquire high-quality QC data at high frequency. This is enabled by the relatively short acquisition time for the images and automatic image analysis. The monitoring system was also used to record and track the rate of MLC-related interlocks and servicing events. MLC performances for two commercially available MLC models have been assessed and the results support monthly test frequency for widely accepted ±1 mm specifications. Higher QC test frequency is however required to maintain tighter specification and in-control behavior.
We consider the possibility that the primordial curvature perturbation is direction-dependent. To first order this is parameterised by a quadrupolar modulation of the power spectrum and results in statistical anisotropy of the CMB, which can be quantified using `bipolar spherical harmonics'. We compute these for the Planck DR2-2015 SMICA map and estimate the noise covariance from Planck Full Focal Plane 9 simulations. A constant quadrupolar modulation is detected with 2.2$σ$ significance, dropping to 2$σ$ when the primordial power is assumed to scale with wave number k as a power law. Going beyond previous work we now allow the spectrum to have arbitrary scale-dependence. Our non-parametric reconstruction then suggests several spectral features, the most prominent at k ~ 0.006 Mpc -1 . When a constant quadrupolar modulation is fitted to data in the range 0.005 ≤ k/Mpc -1 ≤ 0.008, its preferred directions are found to be related to the cosmic hemispherical asymmetry and the CMB dipole. To determine the significance we apply two test statistics to our reconstructions of the quadrupolar modulation from data, against reconstructions of realisations of noise only. With a test statistic sensitive only to the amplitude of the modulation, the reconstructions from the multipole range 30 ≤ ℓ ≤ 1200 are unusual with 2.1$σ$ significance. With the second test statistic, sensitive also to the direction, the significance rises to 6.9$σ$. Our approach is easily generalised to include other data sets such as polarisation, large-scale structure and forthcoming 21-cm line observations which will enable these anomalies to be investigated further.
The authors present a polynomial chaos (PC)–based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers; while the posteriors of the remaining parameters were barely informative.
The United States Department of Energy's Savannah River Site (SRS) in Aiken, South Carolina, is dedicated to promoting site-level, risk-based inspection practices to maintain a safe and productive work environment. Protective suits are worn by personnel working in contaminated environments. These suits require that cooling be applied to keep the interior temperature within safe and comfortable limits. A vortex tube, also known as the Ranque-Hilsch vortex tube (RHVT), can provide the necessary cooling. As mechanical devices void of moving components, vortex tubes separate a compressed gas into hot and cold streams—the air emerging from the “hot” end reaching a temperature of 433.2 K and the air emerging from the “cold” end reaching a temperature of 241.5 K (Hilsch, 1946, “Die Expansion Von Gasen Im Zentrifugalfeld Als Kälteprozeß,” Z. Für Naturforsch., 1, pp. 208–214). Routing the cold stream of the vortex tube to the user's protective suit facilitates the required cooling. Vortex tubes currently in use at SRS are preset, through modification solely by and within the SRS respiratory equipment facility (REF), to provide a temperature reduction between 22.2 and 25.0 K. When a new model of vortex tube capable of user adjustment during operation recently became available, prototype testing was conducted for product comparison. Ultimately, it was identified that similar cooling performance between the old and new models is achievable. Production units were acquired to be subjected to complete product analysis at SRS utilizing a statistical test plan. Finally, the statistical test plan, data, thermodynamic calculations, and conclusions were reviewed to determine acceptability for site use.
In this study, we measure steady-state two-phase supercritical CO 2 -brine relative permeabilities in a 61 cm-long Berea sandstone core at three different conditions (408C and 12.41 MPa, 408C and 8.27 MPa, and 608C and 12.41 MPa) under primary drainage. We use pressure taps to obtain pressure drops of individual sections of the core, and X-ray Computed Tomography (CT) to obtain in situ saturation profiles, which together help to mitigate the capillary end effect. We include previously measured relative permeabilities at 208C and 10.34 MPa, and compare all the data using both an eye-test and a statistical test. We find no appreciable temperature and pressure dependence of CO 2 relative permeability within 20–608C and 8.27–12.41 MPa. We find slight changes in the brine relative permeability between supercritical CO 2 conditions (40–608C and 8.27–12.41 MPa) and the liquid CO 2 condition (208C and 10.34 MPa). The temperature and pressure independence of CO 2 relative permeability has been previously recognized and reassured in this work using a capillary-effect-free method. This allows one to use a single CO 2 relative permeability curve in modeling two-phase CO 2 flow within 20–608C and 8.27–12.41 MPa.
We demonstrate a novel high speed and multi-bit optical quantum random number generator by continuously measuring arrival time of photons with a common starting point. To obtain the unbiased and post-processing free random bits, the measured photon arrival time is converted into the sum of integral multiple of a fixed period and a phase time. Theoretical and experimental results show that the phase time is an independent and uniform random variable. A random bit extraction method by encoding the phase time is proposed. An experimental setup has been built and the unbiased random bit generation rate could reach 128 Mb/s, with random bit generation efficiency of 8 bits per detected photon. The random numbers passed all tests in the statistical test suite.
Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet.