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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Universal Power-law Prescription for Variability from Synthetic Images of Black Hole Accretion Flows

We present a framework for characterizing the spatiotemporal power spectrum of the variability expected from the horizon-scale emission structure around supermassive black holes, and we apply this framework to a library of general relativistic magnetohydrodynamic (GRMHD) simulations and associated general relativistic ray-traced images relevant for Event Horizon Telescope (EHT) observations of Sgr A*. We find that the variability power spectrum is generically a red-noise process in both the temporal and spatial dimensions, with the peak in power occurring on the longest timescales and largest spatial scales. When both the time-averaged source structure and the spatially integrated light-curve variability are removed, the residual power spectrum exhibits a universal broken power-law behavior. On small spatial frequencies, the residual power spectrum rises as the square of the spatial frequency and is proportional to the variance in the centroid of emission. Beyond some peak in variability power, the residual power spectrum falls as that of the time-averaged source structure, which is similar across simulations; this behavior can be naturally explained if the variability arises from a multiplicative random field that has a steeper high-frequency power-law index than that of the time-averaged source structure. We briefly explore the ability of power spectral variability studies to constrain physical parameters relevant for the GRMHD simulations, which can be scaled to provide predictions for black holes in a range of systems in the optically thin regime. We present specific expectations for the behavior of the M87* and Sgr A* accretion flows as observed by the EHT.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Koster, Randal↗
NASA NTRS2014

Generalized Linear Covariance Analysis

This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

Navagation↗
NASA NTRS2014

Generalized Linear Covariance Analysis

This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

n/a↗
NASA NTRS1998

Lidar Measurements of Tropospheric Wind Profiles with the Double Edge Technique

Research has established the importance of global tropospheric wind measurements for large scale improvements in numerical weather prediction. In addition, global wind measurements provide data that are fundamental to the understanding and prediction of global climate change. These tasks are closely linked with the goals of the NASA Earth Science Enterprise and Global Climate Change programs. NASA Goddard has been actively involved in the development of direct detection Doppler lidar methods and technologies to meet the wind observing needs of the atmospheric science community. A variety of direct detection Doppler wind lidar measurements have recently been reported indicating the growing interest in this area. Our program at Goddard has concentrated on the development of the edge technique for lidar wind measurements. Implementations of the edge technique using either the aerosol or molecular backscatter for the Doppler wind measurement have been described. The basic principles have been verified in lab and atmospheric lidar wind experiments. The lidar measurements were obtained with an aerosol edge technique lidar operating at 1064 nm. These measurements demonstrated high spatial resolution (22 m) and high velocity sensitivity (rms variances of 0.1 m/s) in the planetary boundary layer (PBL). The aerosol backscatter is typically high in the PBL and the effects of the molecular backscatter can often be neglected. However, as was discussed in the original edge technique paper, the molecular contribution to the signal is significant above the boundary layer and a correction for the effects of molecular backscatter is required to make wind measurements. In addition, the molecular signal is a dominant source of noise in regions where the molecular to aerosol ratio is large since the energy monitor channel used in the single edge technique measures the sum of the aerosol and molecular signals. To extend the operation of the edge technique into the free troposphere we have developed a variation of the edge technique called the double edge technique. In this paper a ground based aerosol double edge lidar is described and the first measurements of wind profiles in the free troposphere obtained with this lidar will be presented.

Gentry, Bruce M.↗
NASA NTRS1996

Simulation Study Using a New Type of Sample Variance

We evaluate with simulated data a new type of sample variance for the characterization of frequency stability. The new statistic (referred to as TOTALVAR and its square root TOTALDEV) is a better predictor of long-term frequency variations than the present sample Allan deviation. The statistical model uses the assumption that a time series of phase or frequency differences is wrapped (periodic) with overall frequency difference removed. We find that the variability at long averaging times is reduced considerably for the five models of power-law noise commonly encountered with frequency standards and oscillators.

Howe, D. A.↗
NASA NTRS2008

Generalized Linear Covariance Analysis

We review and extend in two directions the results of prior work on generalized covariance analysis methods. This prior work allowed for partitioning of the state space into "solve-for" and "consider" parameters, allowed for differences between the formal values and the true values of the measurement noise, process noise, and a priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and a priori solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator s anchor time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the "variance sandpile" and the "sensitivity mosaic," and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

Carpenter, J. Russell↗
DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗
NASA NTRS1992

Estimation of surface heat and moisture fluxes over a prairie grassland. II - Two-dimensional time filtering and site variability

The behavior of in situ measurements of surface fluxes obtained during FIFE 1987 is examined by using correlative and spectral techniques in order to assess the significance of fluctuations on various time scales, from subdiurnal up to synoptic, intraseasonal, and annual scales. The objectives of this analysis are: (1) to determine which temporal scales have a significant impact on areal averaged fluxes and (2) to design a procedure for filtering an extended flux time series that preserves the basic diurnal features and longer time scales while removing high frequency noise that cannot be attributed to site-induced variation. These objectives are accomplished through the use of a two-dimensional cross-time Fourier transform, which serves to separate processes inherently related to diurnal and subdiurnal variability from those which impact flux variations on the longer time scales. A filtering procedure is desirable before the measurements are utilized as input with an experimental biosphere model, to insure that model based intercomparisons at multiple sites are uncontaminated by input variance not related to true site behavior. Analysis of the spectral decomposition indicates that subdiurnal time scales having periods shorter than 6 hours have little site-to-site consistency and therefore little impact on areal integrated fluxes.

Crosson, William L.↗
DOE OSTI2020

Uncertainty amplification due to density/refractive index gradients in background-oriented schlieren experiments

Here, we theoretically analyze the effect of density/refractive index gradients on the measurement precision of background-oriented schlieren (BOS) experiments by deriving the Cramer–Rao lower bound (CRLB) for the 2D centroid estimation process. A model is derived for the diffraction limited image of a dot viewed through a medium containing density gradients that includes the effect of the experimental parameters such as the magnification and f-number. It is shown using the model that nonlinearities in the density gradient field lead to blurring of the dot image. This blurring amplifies the effect of image noise on the centroid estimation process, leading to an increase in the CRLB and a decrease in the measurement precision. The ratio of position uncertainties of a dot in the reference and gradient images is shown to be a function of the ratio of the dot diameters and dot intensities. We termed this parameter the amplification ratio (A F ), and a methodology for reporting position uncertainties in tracking-based BOS measurements is proposed. The theoretical predictions of the dot position estimation variance from the CRLB are compared to ray tracing simulations, and agreement is obtained. The uncertainty amplification is also demonstrated on experimental BOS images of flow induced by a spark discharge, where it is seen that regions of high amplification ratio correspond to regions of density gradients. This analysis elucidates the dependence of the position uncertainty on density and refractive index gradient-induced distortion parameters, provides a methodology for accounting its effect on uncertainty quantification and provides a framework for optimizing experiment design.

42 ENGINEERING↗
NASA NTRS2019

Star Tracker Accuracy Improvement and Optimization for Attitude Measurement in Three-Axis

High precision attitude measurement systems obviate the need for the beacon from the receiver making it possible for the spacecraft to beam a laser communications signal to a ground station without the ground station advertising its location. The research presented targets new detection and estimation methods to improve the accuracy in locating stars on a focal plane detector, and an understanding of the effects of changes in the optics design parameters and aberration, including defocus, on the navigation solution itself. This understanding can lead to an optimization of the attitude solution with respect to those optics realm parameter changes. The methodology discussed includes the development of a model of a current star tracker system. Using this model, multiple algorithms are implemented, including a multi-hypothesis method (MHT), to detect and estimate the position of the stars on the focal plane detector. It will be shown that using the MHT for detection and estimation, a greater accuracy can be found for each star estimation from more traditional detection and estimation algorithms. The approach then uses the model to develop statistics of the star tracker and the attitude estimation outputs to understand the accuracy, or variance, of the system's attitude solution. This solution is repeated for a range of defocus aberration, and a lower limit to the variance of the attitude solution is shown. A Cramer Rao lower bound solution is derived for the star tracker system and the results are compared to the Monte Carlo analysis from the model and shown to correlate very well. The approach uses a star image not as a Gaussian spot on the focal plane as done in previous work, and use of an image that includes the effects of aberrations of the optic system, and the effects of under-sampling and noise from the focal plane detector as well. Analysis includes exploring a star tracker's accuracy improvement through the combination of focus error and under-sampling effects alone, possibly contradicting conventional wisdom and approaches.

Lichter, Michael J.↗
DOE OSTI2024

Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Finding Real Uncertainties From Physical Simulations

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1982

Supervision of dynamic systems: Monitoring, decision-making and control

Effects of task variables on the performance of the human supervisor by means of modelling techniques are discussed. The task variables considered are: The dynamics of the system, the task to be performed, the environmental disturbances and the observation noise. A relationship between task variables and parameters of a supervisory model is assumed. The model consists of three parts: (1) The observer part is thought to be a full order optimal observer, (2) the decision-making part is stated as a set of decision rules, and (3) the controller part is given by a control law. The observer part generates, on the basis of the system output and the control actions, an estimate of the state of the system and its associated variance. The outputs of the observer part are then used by the decision-making part to determine the instants in time of the observation actions on the one hand and the controls actions on the other. The controller part makes use of the estimated state to derive the amplitude(s) of the control action(s).

White, T. N.↗
NASA NTRS1996

Comparison of New Methods for Assessing Community Response to High Energy Impulsive Sounds

The latest CHABA Working Group to have reviewed published information about the effects of high energy impulsive sounds (such as sonic booms) on communities has recommended abandonment of the dosage-response relationship identified by its predecessor in favor of two alternate prediction method. Both of the new assessment methods continue to rely on C-weighted measurements of impulsive sounds One of the two assessment methods retains the standard assumptions of the 'equal energy hypothesis' (the notion that annoyance is governed simply by the product of level, duration, and number noise events), and further assumes that the rate of growth of the prevalence of annoyance is proportional to the rate of growth of loudness with level. The other assessment method, however, assumes a level dependent (non-equal energy) summation of the C-weighted sound exposure levels of individual impulsive events. Since predictions of the second method are distribution-dependent, they are not readily represents graphically in the form of a single dosage-response function. The effects on annoyance predictions of variance in distributions of CSEL values of impulsive sounds are explored in this presentation.

Fidell, Sanford↗
DOE OSTI2023

Optimal Frequency-domain Analysis for Spacecraft Time Series: Introducing the Missing-data Multitaper Power Spectrum Estimator

While the Lomb–Scargle periodogram is foundational to astronomy, it has a significant shortcoming: the variance in the estimated power spectrum does not decrease as more data are acquired. Statisticians have a 60 yr history of developing variance-suppressing power spectrum estimators, but most are not used in astronomy because they are formulated for time series with uniform observing cadence and without seasonal or daily gaps. Here we demonstrate how to apply the missing-data multitaper power spectrum estimator to spacecraft data with uniform time intervals between observations but missing data during thruster fires or momentum dumps. The F-test for harmonic components may be applied to multitaper power spectrum estimates to identify statistically significant oscillations that would not rise above a white noise–based false alarm probability. Multitapering improves the dynamic range of the power spectrum estimate and suppresses spectral window artifacts. We show that the multitaper–F-test combination applied to Kepler observations of KIC 6102338 detects differential rotation without requiring iterative sinusoid fitting and subtraction. Significant signals reside at harmonics of both fundamental rotation frequencies and suggest an antisolar rotation profile. Next we use the missing-data multitaper power spectrum estimator to identify the oscillation modes responsible for the complex "scallop-shell" shape of the K2 light curve of EPIC 203354381. We argue that multitaper power spectrum estimators should be used for all time series with regular observing cadence.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1979

Carrier phase recovery performance for PN-spread TDRSS link with radio-frequency interference

The carrier-phase recovery performance of a Costas loop for a nonlinear satellite channel with uplink and downlink noise is analyzed, and the extension of these results to the case where uplink radio-frequency interference (RFI) is present is considered. The signal format here is binary phase-shift-keyed (BPSK) or unbalanced quaternary phase-shift-keyed (UQPSK), the signals being either the non-return-to-zero (NRZ) or biphase (Manchester) type. With UQPSK, at least one data stream must be PN-spread. The channel comprises additive white Gaussian noise (WGN), a wideband filter, a bandpass nonlinearity and more additive WGN. The RFI being considered is pulsed continuous-wave, pulsed WGN, or a combination of the two. It is noted that the Tracking and Data Relay Satellite System (TDRSS) will be subjected to RFI from certain locations, especially in the S-band of frequencies. The characteristics of this link are generalized to form the assumptions underlying this analysis. The analytical results given include the S-curve of the equivalent loop and the phase-error variance of the linearized loop.

Mckenzie, T. M.↗
NASA NTRS1992

Gravity and count probabilities in an expanding universe

The time evolution of nonlinear clustering on large scales in cold dark matter, hot dark matter, and white noise models of the universe is investigated using N-body simulations performed with a tree code. Count probabilities in cubic cells are determined as functions of the cell size and the clustering state (redshift), and comparisons are made with various theoretical models. We isolate the features that appear to be the result of gravitational instability, those that depend on the initial conditions, and those that are likely a consequence of numerical limitations. More specifically, we study the development of skewness, kurtosis, and the fifth moment in relation to variance, the dependence of the void probability on time as well as on sparseness of sampling, and the overall shape of the count probability distribution. Implications of our results for theoretical and observational studies are discussed.

Bouchet, Francois R.↗