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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

SIM-Lite Mission Spectral Calibration Sensitivities and Refinements

SIM-Lite missions will perform astrometry at microarcsecond accuracy using star light interferometry. For typical baselines that are shorter than 10 meters, this requires to measure optical path difference (OPD) accurate to tens of picometers calling for highly accurate calibration. A major challenge is to calibrate the star spectral dependency in fringe measurements -- the spectral calibration. Previously, we have developed a spectral calibration and estimation scheme achieving picometer level accuracy. In this paper, we present the improvements regarding the application of this scheme from sensitivity studies. Data from the SIM Spectral Calibration Development Unit (SCDU) test facility shows that the fringe OPD is very sensitive to pointings of both beams from the two arms of the interferometer. This sensitivity coupled with a systematic pointing error provides a mechanism to explain the bias changes in 2007. Improving system alignment can effectively reduce this sensitivity and thus errors due to pointing errors. Modeling this sensitivity can lead to further improvement in data processing. We then investigate the sensitivity to a model parameter, the bandwidth used in the fringe model, which presents an interesting trade between systematic and random errors. Finally we show the mitigation of calibration errors due to system drifts by interpolating instrument calibrations. These improvements enable us to use SCDU data to demonstrate that SIM-Lite missions can meet the 1pm noise floor requirement for detecting earth-like exoplanets.

wave front aberration↗
NASA NTRS1996

SeaWiFS Technical Report Series: SeaWiFS Calibration and Validation Quality Control Procedures - Volume 38

This document provides five brief reports that address several quality control procedures under the auspices of the Calibration and Validation Element (CVE) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. Chapter 1 describes analyses of the 32 sensor engineering telemetry streams. Anomalies in any of the values may impact sensor performance in direct or indirect ways. The analyses are primarily examinations of parameter time series combined with statistical methods such as auto- and cross-correlation functions. Chapter 2 describes how the various onboard (solar and lunar) and vicarious (in situ) calibration data will be analyzed to quantify sensor degradation, if present. The analyses also include methods for detecting the influence of charged particles on sensor performance such as might be expected in the South Atlantic Anomaly (SAA). Chapter 3 discusses the quality control of the ancillary environmental data that are routinely received from other agencies or projects which are used in the atmospheric correction algorithm (total ozone, surface wind velocity, and surface pressure; surface relative humidity is also obtained, but is not used in the initial operational algorithm). Chapter 4 explains the procedures for screening level-, level-2, and level-3 products. These quality control operations incorporate both automated and interactive procedures which check for file format errors (all levels), navigation offsets (level-1), mask and flag performance (level-2), and product anomalies (all levels). Finally, Chapter 5 discusses the match-up data set development for comparing SeaWiFS level-2 derived products with in situ observations, as well as the subsequent outlier analyses that will be used for evaluating error sources.

Hooker, Stanford B.↗
NASA NTRS2013

Parameter Estimation for Compact Binary Coalescence Signals with the First Generation Gravitational-Wave Detector Network

Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.

Aasi, J.↗
DOE OSTI2021

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1990

A synopsis of Langley Research Center's lidar effort for the 1986 FIRE IFO

The lidar data obtained by the Langley Aircraft Lidar in October 1986 in Wisconsin is being reduced in a transparent, simple fashion and will be published in its reduced form in a NASA Reference Publication (RP). This reduced data will also be submitted to the FIRE data archives. Some of this reduced data will be presented at the FIRE FSET Workshop to acquaint the science team with the data format to be used in the archive and the upcoming catalog contained in the RP. A new method was utilized in Wisconsin for obtain the depolarization ratio of aerosols. This method involves using a half-wave plate to calibrate the lidar under field conditions. The theory behind this technique will be presented at this workshop as well as some of the lidar calibration results. The lidar calibration will be utilized in interpreting some of the dual polarization lidar data obtained during the IFO in Wisconsin. Some of these data are also discussed. A continuous wave laser lab-type lidar simulator was constructed during the previous year. One of the primary reasons for the construction of the simulator was to attempt dual-polarization lidar-like calibrations under laboratory, rather than field conditions. The data collected by this system was used to experimentally check and thus, inspire confidence in the algorithms being used to interpret the lidar data obtained in the field. A computer program which simulates noisy lidar data was used as a part of this effort in order to obtain some feel for the noise in the inversion parameters as a function of noise in the actual measurements. The lidar simulation will be described in addition to presenting some of the lab-generated calibration data.

Alvarez, Jose M.↗
DOE OSTI2023

Internal calibration of transient kinetic data via machine learning

The temporal analysis of products (TAP) reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including residence time distributions, kinetic coefficients, number of active sites, reaction mechanism, etc. However, as with any measurement device, the TAP reactor signal is convoluted with noise and drift is common. In order to reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced type of analysis. This preprocessing includes baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The traditional methodology of preprocessing requires significant user discretion and reliance on separate calibration experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to understand the noise and drift that is being generated within and between experiments for enhancement of the chemical kinetic signal. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing approach by eliminating the need for separate calibration experiments or heuristic input from the user.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of a hard X-ray self-seeding diamond crystal orientation

We present a method to accurately control the photon energies for hard X-ray Self-seeding schemes with a single crystal monochromator in transmissive geometry. The energy calibration is performed by measuring which pairs of the machine pitch and yaw angles for different crystallographic planes reflect the X-ray at the same wavelength. The free parameters of an analytical formula for the self-seeding energies are determined by fitting the observed intersections and the normalized derivative with respect to the pitch and yaw angles in the observed intersections. The method requires a hard X-ray spectrometer, but it does not rely on its absolute energy calibration. Instead, identifying the self-seeded energies above the SASE background or the monochromatic notches within the SASE bandwidth is sufficient for the calibration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2013

Application and Evaluation of a Snowmelt Runoff Model in the Tamor River Basin, Eastern Himalaya Using a Markov Chain Monte Carlo (MCMC) Data Assimilation Approach

Previous studies have drawn attention to substantial hydrological changes taking place in mountainous watersheds where hydrology is dominated by cryospheric processes. Modelling is an important tool for understanding these changes but is particularly challenging in mountainous terrain owing to scarcity of ground observations and uncertainty of model parameters across space and time. This study utilizes a Markov Chain Monte Carlo data assimilation approach to examine and evaluate the performance of a conceptual, degree-day snowmelt runoff model applied in the Tamor River basin in the eastern Nepalese Himalaya. The snowmelt runoff model is calibrated using daily streamflow from 2002 to 2006 with fairly high accuracy (average Nash-Sutcliffe metric approx. 0.84, annual volume bias <3%). The Markov Chain Monte Carlo approach constrains the parameters to which the model is most sensitive (e.g. lapse rate and recession coefficient) and maximizes model fit and performance. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall compared with simulations using observed station precipitation. The average snowmelt contribution to total runoff in the Tamor River basin for the 2002-2006 period is estimated to be 29.7+/-2.9% (which includes 4.2+/-0.9% from snowfall that promptly melts), whereas 70.3+/-2.6% is attributed to contributions from rainfall. On average, the elevation zone in the 4000-5500m range contributes the most to basin runoff, averaging 56.9+/-3.6% of all snowmelt input and 28.9+/-1.1% of all rainfall input to runoff. Model simulated streamflow using an interpolated precipitation data set decreases the fractional contribution from rainfall versus snowmelt compared with simulations using observed station precipitation. Model experiments indicate that the hydrograph itself does not constrain estimates of snowmelt versus rainfall contributions to total outflow but that this derives from the degree-day melting model. Lastly, we demonstrate that the data assimilation approach is useful for quantifying and reducing uncertainty related to model parameters and thus provides uncertainty bounds on snowmelt and rainfall contributions in such mountainous watersheds.

runoff↗
NASA NTRS1987

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 1: Dynamic models and computer simulations for the ERBE nonscanner, scanner and solar monitor sensors

Dynamic models and computer simulations were developed for the radiometric sensors utilized in the Earth Radiation Budget Experiment (ERBE). The models were developed to understand performance, improve measurement accuracy by updating model parameters and provide the constants needed for the count conversion algorithms. Model simulations were compared with the sensor's actual responses demonstrated in the ground and inflight calibrations. The models consider thermal and radiative exchange effects, surface specularity, spectral dependence of a filter, radiative interactions among an enclosure's nodes, partial specular and diffuse enclosure surface characteristics and steady-state and transient sensor responses. Relatively few sensor nodes were chosen for the models since there is an accuracy tradeoff between increasing the number of nodes and approximating parameters such as the sensor's size, material properties, geometry, and enclosure surface characteristics. Given that the temperature gradients within a node and between nodes are small enough, approximating with only a few nodes does not jeopardize the accuracy required to perform the parameter estimates and error analyses.

Halyo, Nesim↗
NASA NTRS1992

Performance characteristics of STIS detectors

We report quantum efficiency measurements of back-illuminated, ion-implanted, laser-annealed charge coupled devices (CCD's) in the wavelength range 13-10,000 A. The equivalent quantum efficiency (EQE = effective photons detected per incident photon) ranges from a minimum of 5 percent as 1216 A to a maximum of 87 percent at 135 A. Using a simple relationship for the charge collection efficiency of the CCD pixels as a function of depth, we present a semi-empirical model with few parameters which reproduces our measurements with a fair degree of accuracy. The advantage of this model is that is can be used to predict CCD QE performance for shallow backside implanted devices without detailed solution of a system of differential equations, as in conventional approaches, and yields a simple analytic form for the charge collection efficiency which is adequate for detector calibration purposes. Making detailed assumptions about the dopant profile, we also solve the carrier density and continuity equations in order to relate our semi-empirical model parameters to surface and bulk device properties. The latter procedure helps to better establish device processing parameters for a given level of CCD QE performance.

Stern, Robert A.↗
DOE OSTI2021

Combination of cluster number counts and two-point correlations: validation on mock Dark Energy Survey

ABSTRACT We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy–cluster cross-correlations, cluster autocorrelations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year 1 (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES-Y1 experiment. We compare the χ2 values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of DES-Y1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalogue level simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Investigation of techniques for correcting ERTS data for solar and atmospheric effects

The author has identified the following significant results. The design and fabrication of five radiant power measuring instruments (RPMI) for ERTS ground truth have been completed. These instruments will be deployed in concert with ERTS overflights to obtain radiometric measurements needed to determine solar and atmospheric parameters that effect ERTS radiance measurements. With these parameters, the accuracy and capability of various procedures for transforming ERTS data into absolute target reflectance signatures will be evaluated. The RPMI is a rugged, hand-carried instrument accurately calibrated to measure both downwelling and reflected radiance within each ERTS multispectral scanner band. A foldover handle permits a quick change from wide angle global or sky irradiance measurements to narrow angle radiance measurements from sky and ground targets. These measurements yield ground truth site reflectance and permit calculation of additional parameters such as beam transmittance between spacecraft and ground, and path radiance (path reflectance).

Rogers, R. H.↗
DOE OSTI2022

Calibration and Localization of Optically Pumped Magnetometers Using Electromagnetic Coils

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions. We then localize and calibrate an OPM by minimizing the sum of squared errors between the model signals and the OPM responses to the coil fields. We show that by using homogeneous and first-order gradient fields, the OPM sensor parameters (gain, position, and orientation) can be obtained from a set of linear equations with pseudo-inverses of two matrices. The currents that should be applied to the coils for approximating these low-order field components can be determined based on the VSH models. Computationally simple initial estimates of the OPM sensor parameters follow. As a first test of the method, we placed a fluxgate magnetometer at multiple positions and estimated the RMS position, orientation, and gain errors of the method to be 1.0 mm, 0.2°, and 0.8%, respectively. Lastly, we calibrated a 48-channel OPM array. The accuracy of the OPM calibration was tested by using the OPM array to localize magnetic dipoles in a phantom, which resulted in an average dipole position error of 3.3 mm. The results demonstrate the feasibility of using electromagnetic coils to calibrate and localize OPMs for MEG.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Measuring and simulating ice–ablator mix in inertial confinement fusion

Fuel–ablator mix has been established as a major performance degrading effect in the burning plasma regime of recent inertial confinement fusion (ICF) experiments. As such, the study of fuel–ablator mix with experiments and simulations can provide valuable insight for our understanding of these experiments and establish a path for even higher yields and increased robustness. Here we present a novel high-yield experimental ICF design that is motivated by recent experiments measuring ice–ablator mix with a CH ablator instead of a high-density carbon (HDC) ablator. Here we review these experiments in more detail and describe the modeling assumptions and parameters used to obtain agreement with the data from implosion and burn simulations with mix. Using this mix model calibrated a posteriori to the experimental data, we design an implosion that uses a CH ablator that is predicted to achieve better performance than a recent experiment that achieved net target gain of 1.5 in HDC. Because hydrodynamic instabilities are greatly reduced with this new design, we also expect a high reproducibility at the same implosion adiabat as current record yield experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Vibration and EMF Backgrounds at NEXUS

NEXUS (Northwestern EXperimental Underground Site) is a dark matter detector prototyping and calibration facility at Fermilab. It is part of the SuperCDMS collaboration, which is focused on exploring light-WIMP parameter space. The NEXUS cryostat, typically operated at temperatures of about 10 mK, is located 107 meters underground in the MINOS Near Detector Hall to reduce cosmic ray backgrounds. We characterized the vibrational and electro-magnetic frequency (EMF) backgrounds surrounding the cryostat and investigated how these backgrounds transfer into the detector itself.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Complementary Study of Radiative Heat Transfer and Flow Physics from Moderate-scale Hydrocarbon Pool Fire Simulations

As part of the Advanced Simulation and Computing Verification and Validation (ASCVV) program, a 0.3-m diameter hydrocarbon pool fire with multiple fuels was modeled and simulated. In the study described in this report, systematic examination was performed on the radiation model used in a series of coupled Fuego/Nalu simulations. A calibration study was done with a medium-scale methanol pool fire and the effect of calibration traced throughout the radiation model. This analysis provided a more detailed understanding of the effect of radiation model parameters on each other and on other quantities in the simulations. Heptane simulation results were also examined using this approach and possible areas for further improvement of the models were identified. The effect of soot on radiative losses was examined by comparing heptane and methanol results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

GEANT4 code for DANCE with NEUANCE detector-response simulations

A new GEANT4 model for DANCE with NEUANCE has been developed to account for the change in the DANCE configuration when NEUANCE is installed. In this model, the energy resolution and the shape of the threshold of the individual DANCE detectors are used as an input to provide realistic representation of the measured γ-ray spectra. In addition, the distance from each detector to the target is defined in an input file as well as the physical presence of the a detector in the DANCE and NEUANCE arrays. The azimuthal angle of NEUANCE is also an external parameter to be tuned to represent the measurement. Simulated spectra are compared with measurements with standard γ-ray calibrated sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Computationally efficient subglacial drainage modelling using Gaussian process emulators: GlaDS-GP v1.0

Subglacial drainage models represent water flow at the ice–bed interface through coupled distributed and channelized systems to determine water pressure, discharge, and drainage system geometry. While they are used to understand processes such as the relationship between surface melt and ice flow, the number of uncertain model parameters and the computational cost of running models makes it difficult to adequately explore the high-dimensional parameter space and evaluate uncertainty in model predictions. Here, we develop Gaussian process (GP) emulators that make fast predictions with associated uncertainty of subglacial drainage model outputs. Using a truncated principal component (PC) basis representation, we construct a GP emulator for diurnally averaged subglacial water pressure. We also explore emulation of scalar variables describing drainage efficiency and configuration. We train the emulators using ensembles of up to 512 simulations varying eight parameters of the Glacier Drainage System (GlaDS) model on a synthetic domain intended to represent an ice-sheet margin. The emulators make predictions ∼ 1000 times faster than GlaDS simulations, with errors <3 % for the water pressure field and ∼ 5 %–9 % for drainage efficiency and configuration. We apply the emulators to explore the eight-dimensional parameter space by computing variance-based parameter sensitivity indices, finding that three parameters (ice flow coefficient, bed bump aspect ratio, and the subglacial cavity system conductivity) explain 90 % of the variance in modelled water pressure in response to parameter changes. The GP emulator approach described here is well suited to integrating observational data with models to make calibrated, credible predictions of subglacial drainage.

58 GEOSCIENCES↗