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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1976

LACIE performance predictor FOC users manual

The LACIE Performance Predictor (LPP) is a computer simulation of the LACIE process for predicting worldwide wheat production. The simulation provides for the introduction of various errors into the system and provides estimates based on these errors, thus allowing the user to determine the impact of selected error sources. The FOC LPP simulates the acquisition of the sample segment data by the LANDSAT Satellite (DAPTS), the classification of the agricultural area within the sample segment (CAMS), the estimation of the wheat yield (YES), and the production estimation and aggregation (CAS). These elements include data acquisition characteristics, environmental conditions, classification algorithms, the LACIE aggregation and data adjustment procedures. The operational structure for simulating these elements consists of the following key programs: (1) LACIE Utility Maintenance Process, (2) System Error Executive, (3) Ephemeris Generator, (4) Access Generator, (5) Acquisition Selector, (6) LACIE Error Model (LEM), and (7) Post Processor.

Source record↗
NASA NTRS1990

Estimation in a discrete tail rate family of recapture sampling models

In the context of recapture sampling design for debugging experiments the problem of estimating the error or hitting rate of the faults remaining in a system is considered. Moment estimators are derived for a family of models in which the rate parameters are assumed proportional to the tail probabilities of a discrete distribution on the positive integers. The estimators are shown to be asymptotically normal and fully efficient. Their fixed sample properties are compared, through simulation, with those of the conditional maximum likelihood estimators.

Gupta, Rajan↗
DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a number of extreme metrics. While we exploit existing large ensembles, our perspective is that of a modeling center wanting to estimate a priori such sizes on the basis of an existing small ensemble (we assume the availability of only five members here). We therefore ask if such a small-size ensemble is sufficient to estimate accurately the population variance (i.e., the ensemble internal variability) and then apply a well-established formula that quantifies the expected error in the estimation of the population mean (i.e., the forced component) as a function of the sample size n, here taken to mean the ensemble size. We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extremes as a function of n by plugging into the formula an estimate of the population variance derived on the basis of five members. For a range of spatial and temporal scales, forcing levels (we use simulations under Representative Concentration Pathway 8.5) and two models considered here as our proof of concept, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, and this remains true when statistical inference through extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. Using the F test, we find that estimates on the basis of only 5 or 10 ensemble members accurately represent the full ensemble variance even when the analysis is conducted at the grid-point scale. The detection of changes in the variance when comparing different times along the simulation, especially for the precipitation-based metrics, requires larger sizes but not larger than 15 or 20 members. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, such as physics parameter settings, when designing ensemble simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Mass variance from archival X-ray properties of Dark Energy Survey Year-1 galaxy clusters

ABSTRACT Using archival X-ray observations and a lognormal population model, we estimate constraints on the intrinsic scatter in halo mass at fixed optical richness for a galaxy cluster sample identified in Dark Energy Survey Year-One (DES-Y1) data with the redMaPPer algorithm. We examine the scaling behaviour of X-ray temperatures, TX, with optical richness, λRM, for clusters in the redshift range 0.2 < z < 0.7. X-ray temperatures are obtained from Chandra and XMM observations for 58 and 110 redMaPPer systems, respectively. Despite non-uniform sky coverage, the TX measurements are $\gt 50{{\ \rm per\ cent}}$ complete for clusters with λRM > 130. Regression analysis on the two samples produces consistent posterior scaling parameters, from which we derive a combined constraint on the residual scatter, $\sigma _{\ln T \, |\, \lambda }= 0.275 \pm 0.019$. Joined with constraints for TX scaling with halo mass from the Weighing the Giants program and richness–temperature covariance estimates from the LoCuSS sample, we derive the richness-conditioned scatter in mass, $\sigma _{\ln M \, |\, \lambda }= 0.30 \pm 0.04\, _{({\rm stat})} \pm 0.09\, _{({\rm sys})}$, at an optical richness of approximately 100. Uncertainties in external parameters, particularly the slope and variance of the TX–mass relation and the covariance of TX and λRM at fixed mass, dominate the systematic error. The $95{{\ \rm per\ cent}}$ confidence region from joint sample analysis is relatively broad, $\sigma _{\ln M \, |\, \lambda }\in [0.14, \, 0.55]$, or a factor 10 in variance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Tale of Two Disks: Mapping the Milky Way with the Final Data Release of APOGEE

We present new maps of the Milky Way disk showing the distribution of metallicity ([Fe/H]), α-element abundances ([Mg/Fe]), and stellar age, using a sample of 66,496 red giant stars from the final data release (DR17) of the Apache Point Observatory Galactic Evolution Experiment survey. We measure radial and vertical gradients, quantify the distribution functions for age and metallicity, and explore chemical clock relations across the Milky Way for the low-α disk, high-α disk, and total population independently. The low-α disk exhibits a negative radial metallicity gradient of -0.06 ± 0.001 dex kpc -1 , which flattens with distance from the midplane. The high-α disk shows a flat radial gradient in metallicity and age across nearly all locations of the disk. The age and metallicity distribution functions shift from negatively skewed in the inner Galaxy to positively skewed at large radius. Significant bimodality in the [Mg/Fe]–[Fe/H] plane and in the [Mg/Fe]–age relation persist across the entire disk. The age estimates have typical uncertainties of ~0.15 in log(age) and may be subject to additional systematic errors, which impose limitations on conclusions drawn from this sample. Nevertheless, these results act as critical constraints on galactic evolution models, constraining which physical processes played a dominant role in the formation of the Milky Way disk. We discuss how radial migration predicts many of the observed trends near the solar neighborhood and in the outer disk, but an additional more dramatic evolution history, such as the multi-infall model or a merger event, is needed to explain the chemical and age bimodality elsewhere in the Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2022

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: growth rate of structure measurement from cosmic voids

ABSTRACT We present a void clustering analysis in configuration-space using the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 samples. These samples consist of Luminous Red Galaxies (LRGs) combined with the high-redshift tail of the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) DR12 CMASS galaxies (called as LRG+CMASS sample), Emission Line Galaxies (ELGs), and quasars (QSOs). We build void catalogues from the three eBOSS DR16 samples using a ZOBOV-based algorithm, providing 2814 voids, 1801 voids, and 4347 voids in the LRG+CMASS, ELG, and QSO samples, respectively, spanning the redshift range 0.6 < z < 2.2. We measure the redshift space distortions around voids using the anisotropic void-galaxy cross-correlation function and we extract the distortion parameter β. We test the methodology on realistic simulations before applying it to the data, and we investigate all our systematic errors on these mocks. We find βLRG(z = 0.74) = 0.415 ± 0.087, βELG(z = 0.85) = 0.665 ± 0.125 and βQSO(z = 1.48) = 0.313 ± 0.134, for the LRG+CMASS, ELG, and QSO sample, respectively. The quoted errors include systematic and statistical contributions. In order to convert our measurements in terms of the growth rate fσ8, we use consensus values of linear bias from the eBOSS DR16 companion papers, resulting in the following constraints: fσ8(z = 0.74) = 0.50 ± 0.11, fσ8(z = 0.85) = 0.52 ± 0.10, and fσ8(z = 1.48) = 0.30 ± 0.13. Our measurements are consistent with other measurements from eBOSS DR16 using conventional clustering techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Freestream Mach-Number and Temperature Measurement in NASA Langley’s 20-Inch Supersonic Wind Tunnel Using Laser-Induced Thermal Acoustics

We report single-laser-shot (0.3 us) and time-averaged (500 laser shots at 30 Hz repetition rate) measurements of static temperature T and Mach number M in the freestream of NASA Langley’s 20-inch Supersonic Wind Tunnel (SWT), using a nonintrusive optical technique: laser-induced thermal acoustics (LITA). Although the single-shot LITA T measurements show typical standard deviations (SD) of the sample of +- 4% (+- 1-sigma or 68% confidence for random errors), the 1-s averages show SDs of the mean deltaT = +- 0.5 K, or +- 0.3% for flow at T ~ 160 K. The 17-s averages show ± 0.1% SDs of the mean. Both 1-s and 17-s averages agree to within about 1% of SWT’s traditional probe measurements. Additionally, the single-shot LITA M measurements show typical shot-to-shot SDs of the sample of +- 5% (+- 1-sigma), but the 1-s averages indicate 1-sigma SDs of the means deltaM = +- 0.02, or +- 1% for M = 2.0. Extending the time averages to 17 s, SDs of the mean are reduced to about deltaM = +- 0.007, or +- 0.4% (± 1-sigma). The 1-s and 17-s time-averaged LITA-measured Mach numbers also agree with the SWT probe instrumentation to within 1%. Most of the shot-to-shot measurement noise probably arises from the LITA instrument itself (the fundamental limit of Fourier transforming a short-duration data series). Thus the 1-s averages only provide upper limits for the temporal stability of the freestream tunnel flow on 1-s time scales and complement previous work that characterized the SWT spatial uniformity of the flow. They also provide a first noninvasive comparison to the traditional calibrations with physical probes, for both the mean and fluctuating components of the freestream.

Laser-Induced Thermal Acoustics (LITA)↗
DOE OSTI2023

Label-free quantum super-resolution imaging using entangled multi-mode squeezed light

Abstract In this study, we explore the theoretical application of entangled multi-mode squeezed light for label-free optical super-resolution imaging. By generating massively entangled multi-mode squeezed light through an array of balanced beam splitters, using a single-mode squeezed light input, we create a multi-mode quantum light state with exceptional entanglement and noise suppression below the shot noise level. This significantly reduces imaging measurement errors compared to classical coherent state light imaging when the same number of photons are used on the imaging sample. We demonstrate how to optimize the imaging system’s parameters to achieve the Heisenberg imaging error limit, taking into account the number of entangled modes and photons used. We also examine the effects of optical losses in the imaging system, necessitating adjustments to the optimized parameters based on the degree of optical loss. In practical applications, this new quantum imaging approach reduces the number of photons needed to achieve the same image quality by two orders of magnitude compared to classical imaging methods that use non-entangled, non-squeezed coherent state light.

Soh, Daniel (ORCID:0000000325625675)↗
NASA NTRS1996

Model Stellar Atmospheres and Real Stellar Atmospheres and Status of the ATLAS12 Opacity Sampling Program and of New Programs for Rosseland and for Distribution Function Opacity

I discuss errors in theory and in interpreting observations that are produced by the failure to consider resolution in space, time, and energy. I discuss convection in stellar model atmospheres and in stars. Large errors in abundances are possible such as the factor of ten error in the Li abundance for extreme Population II stars. Finally I discuss the variation of microturbulent velocity with depth, effective temperature, gravity, and abundance. These variations must be dealt with in computing models and grids and in any type of photometric calibration. I have also developed a new opacity-sampling version of my model atmosphere program called ATLAS12. It recognizes more than 1000 atomic and molecular species, each in up to 10 isotopic forms. It can treat all ions of the elements up through Zn and the first 5 ions of heavier elements up through Es. The elemental and isotopic abundances are treated as variables with depth. The fluxes predicted by ATLAS12 are not accurate in intermediate or narrow bandpass intervals because the sample size is too small. A special stripped version of the spectrum synthesis program SYNTHE is used to generate the surface flux for the converged model using the line data on CD-ROMs 1 and 15. ATLAS12 can be used to produce improved models for Am and Ap stars. It should be very useful for investigating diffusion effects in atmospheres. It can be used to model exciting stars for H II regions with abundances consistent with those of the H II region. These programs and line files will be distributed on CD-ROMs.

Kurucz, Robert L.↗
NASA NTRS1991

Wiener-matrix image restoration beyond the sampling passband

A finer-than-sampling-lattice resolution image can be obtained using multiresponse image gathering and Wiener-matrix restoration. The multiresponse image gathering weighs the within-passband and aliased signal components differently, allowing the Wiener-matrix restoration filter to unscramble these signal components and restore spatial frequencies beyond the sampling passband of the photodetector array. A multiresponse images can be reassembled into a single minimum mean square error image with a resolution that is sq rt A times finer than the photodetector-array sampling lattice.

Rahman, Zia-Ur↗
DOE OSTI2022

Analyzing rotational bands in odd-mass nuclei using effective field theory and Bayesian methods

We recently developed an Effective Field Theory (EFT) for rotational bands in odd-mass nuclei. Here we use EFT expressions to perform a Bayesian analysis of data on the rotational energy levels of 99 Tc, 155,157 Gd, 159 Dy, 167,169 Er, 167,169 Tm, 183 W, 235 U and 239 Pu. The error model in our Bayesian analysis includes both experimental and EFT truncation uncertainties. It also accounts for the fact that low-energy constants (LECs) at even and odd orders are expected to have different sizes. We use Markov Chain Monte Carlo (MCMC) sampling to explore the joint posterior of the EFT and error-model parameters and show both the LECs and the breakdown scale can be reliably determined. We extract the LECs up to fourth order in the EFT and find that, provided we correctly account for EFT truncation errors in our likelihood, results for lower-order LECs are stable as we go to higher orders. LEC results are also stable with respect to the addition of higher-energy data. We extract the expansion parameter for all the nuclei listed above and find a clear correlation between the extracted and the expected value of the inverse breakdown scale, W, based on the single-particle and vibrational energy scales. However, the W that actually determines the convergence of the EFT expansion is markedly smaller than would be naively expected based on those scales.

EFT↗
NASA NTRS2015

Airborne Lidar-Based Estimates of Tropical Forest Structure in Complex Terrain: Opportunities and Trade-Offs for REDD+

Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.

Airborne lidar↗
DOE OSTI2020

Rapid Determination of Uranium Isotopic Abundance from Cotton Swipes: Direct Extraction via a Planar Surface Reader and Coupling to a Microplasma Ionization Source

The collection of solid particulates and liquids from surfaces by the use of cloth swipes is fairly ubiquitous. In such methods, there is a continuous concern regarding the ability to locate and quantitatively sample the analyte species from the material. In this effort, we demonstrate the initial coupling of an Advion Plate Express plate reader to a liquid sampling–atmospheric pressure glow discharge (LS-APGD) microplasma ionization source with an Orbitrap mass spectrometer to perform uranium isotopic analyses of solution residues on cotton swipes. The Plate Express employs a sampling probe head to engage and seal against the swipe surface. Subsequentially, the analyte residues are desorbed and transported within a 2% HNO 3 electrolyte flow to the ionization source. Quantitative recoveries were observed following a single 30 s extraction step, with the absolute mass sampled per extraction being ~100 ng. While the intrasample variability in the analytical responses for triplicate sampling of the same swipe yield ~30% RSD, this lack of precision is offset by the ability to determine isotope ratios for enriched uranium specimens with a precision of better than 10% RSD. Pooled, intersample precision ( n = 9) was found to be <5%RSD across the various sample compositions. Finally, 235 U/ 238 U determinations (ranging from 0.053 to 1.806) were accurate with errors of <10%, absolute. The 234 U- and 236 U-inclusive ratios were determined with similar accuracy in enriched samples. While the driving force for the effort is in the realm of nuclear nonproliferation efforts, the ubiquitous use of cloth swipes across many application areas could benefit from this convenient approach, including the use of versatile, reduced-format mass spectrometer systems.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2007

Gravity Probe B Timing System and Roll Phase Determination

An oven-controlled crystal oscillator at 16.368 MHz provides clock signals to all GP-B components and synchronizes the data collection, transmission and processing. The sampled science data signals are stamped with the vehicle time, a counter of the 10Hz data strobe divided down from the clock. The GPS receiver supplies an external reference for time transfer between the vehicle time and coordinated universal time. Ground and space flight tests show the time transfer error is within 1 microsecond. The time latency between the effective sample time of science signals and the stamped vehicle time is verified to 1 ms in the ground tests. The GP-B satellite is controlled to roll with a period of 77.5 sec about an axis along the direction to the guide star to average out the disturbance torques fixed to the body of the satellite and reduce the gyroscope readout noise. The roll phase is determined on the ground to high accuracy with the telemetry data from two star trackers and used in the data analysis to separate the drifts of gyroscope spin axes in the orbital plane and perpendicular to the orbital plane. The flight data shows that the roll phase is controlled to within 40 arcsec with a measurement uncertainty of 7 arcsec.

Jie, Li↗