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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Photometric Recalibration of the SDSS Stripe 82 to a Few Millimagnitude Precision with the Stellar Color Regression Method and Gaia EDR3

By combining spectroscopic data from the LAMOST DR7, Sloan Digital Sky Survey (SDSS) DR12, and corrected photometric data from the Gaia EDR3, we apply the stellar color regression (SCR) method to recalibrate the SDSS Stripe 82 standard stars catalog of Ivezic' et al. With a total number of about 30,000 spectroscopically targeted stars, we have mapped out the relatively large and strongly correlated photometric zero-point errors present in the catalog, ~2.5% in the u band and ~1% in the griz bands. Our study also confirms some small but significant magnitude dependence errors in the z band for some charge-coupled devices. Various tests show that we have achieved an internal precision of about 5 mmag in the u band and about 2 mmag in the griz bands, which is about five times better than previous results. We also apply the method to the latest version of the catalog (v4.2), and find modest systematic calibration errors of up to ~1% along the R.A. direction and smaller errors along the decl. direction. The results demonstrate the power of the SCR method when combining spectroscopic data and Gaia photometry in breaking the 1% precision barrier of ground-based photometric surveys. Our work paves the way for the recalibration of the whole SDSS photometric survey and has important implications for the calibration of future surveys. Future implementations and improvements of the SCR method under different situations are also discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

EMISSA (Exploring Millimeter Indicators of Solar-Stellar Activity): I. The initial millimeter–centimeter main-sequence star sample

Due to their wide wavelength coverage across the millimeter to centimeter (mm–cm) range and their increased sensitivity, modern interferometric arrays facilitate observations of the thermal and non-thermal radiation that is emitted from different layers in the outer atmospheres of stars. We study the spectral energy distribution (S obs (ν)) of main-sequence stars based on archival observations in the mm–cm range with the aim to study their atmospheric stratification as a function of stellar type. The main-sequence stars with significant detection in mm bands were identified in the ALMA Science Archive. These data were then complemented with spectral flux data in the extreme ultraviolet to cm range as compiled from various catalogues and observatory archives. We compared the resultant S obs (ν) of each star with a photospheric emission model (S mod (ν)) calculated with the PHOENIX code. The departures of S obs (ν) from S mod (ν) were quantified in terms of a spectral flux excess parameter (ΔS/S mod ) and studied as a function of stellar type. The initial sample consists of 12 main-sequence stars across a broad range of spectral types from A1 to M3.5 and the Sun-as-a-star as reference. The stars with T eff = 3000–7000 K (F–M type) showed a systematically higher S obs (ν) than S mod (ν) in the mm–cm range. Their ΔS/S mod exhibits a monotonic rise with decreasing frequency. The steepness of this rise is higher for cooler stars in the T eff = 3000–7000 K range, although the single fully convective star (T eff ~ 3000 K) in the sample deviates from this trend. Meanwhile, Sobs(ν) of the A-type stars agrees with S mod (ν) within errors. The systematically high ΔS/S mod in F–M stars indicates hotter upper atmospheric layers, that is, a chromosphere and corona in these stars, like for the Sun. The mm–cm ΔS/S mod spectrum offers a way to estimate the efficiency of the heating mechanisms across various outer atmospheric layers in main-sequence stars, and thereby to understand their structure and activity. We emphasise the need for dedicated surveys of main-sequence stars in the mm–cm range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Non-local Thermodynamic Equilibrium Stellar Spectroscopy with 1D and 〈3D〉 Models. I. Methods and Application to Magnesium Abundances in Standard Stars

We determine Mg abundances in six Gaia benchmark stars using theoretical one-dimensional (1D) hydrostatic model atmospheres, as well as temporally and spatially averaged three-dimensional (〈3D〉) model atmospheres. The stars cover a range of T{sub eff} from 4700 to 6500 K, logg from 1.6 to 4.4 dex, and [Fe/H] from −3.0 dex to solar. Spectrum synthesis calculations are performed in local thermodynamic equilibrium (LTE) and in non-LTE (NLTE) using the oscillator strengths recently published by Pehlivan Rhodin et al. We find that: (a) Mg abundances determined from the infrared spectra are as accurate as the optical diagnostics, (b) the NLTE effects on Mg i line strengths and abundances in this sample of stars are minor (although for a few Mg i lines the NLTE effects on abundance exceed 0.6 dex in 〈3D〉 and 0.1 dex in 1D, (c) the solar Mg abundance is 7.56±0.05 dex (total error), in excellent agreement with the Mg abundance measured in CI chondritic meteorites, (d) the 1D NLTE and 〈3D〉 NLTE approaches can be used with confidence to analyze optical Mg i lines in spectra of dwarfs and sub-giants, but for red giants the Mg i 5711 Å line should be preferred, (e) low-excitation Mg i lines are sensitive to the atmospheric structure; for these lines, LTE calculations with 〈3D〉 models lead to significant systematic abundance errors. The methods developed in this work will be used to study Mg abundances of a large sample of stars in the next paper in the series.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

Assessment of the TOPEX altimeter performance using waveform retracking

To assess the accuracy of the TOPEX altimeter data, we have reprocessed the raw altimeter waveform data using more sophisticated algorithms than those implemented in the altimeter hardware. We discuss systematic contamination of the waveform which we have observed and its effect on very long wavelength errors. We conclude that these systematic errors are responsible for a very long wavelength error whose peak-to-peak magnitude for the Ku band altimeter is of the order of 1 cm. We also examine the ability of retracked data to reduce the repeat pass variance and correct for significant wave height (SWH) and acceleration dependent errors. We find that the ground postprocessing contains SWH dependent biases which depend on the altimeter fine height correction.

Rodriguez, Ernesto↗
DOE OSTI2013

Statistical Assessment of Proton Treatment Plans Under Setup and Range Uncertainties

Purpose: To evaluate a method for quantifying the effect of setup errors and range uncertainties on dose distribution and dose–volume histogram using statistical parameters; and to assess existing planning practice in selected treatment sites under setup and range uncertainties. Methods and Materials: Twenty passively scattered proton lung cancer plans, 10 prostate, and 1 brain cancer scanning-beam proton plan(s) were analyzed. To account for the dose under uncertainties, we performed a comprehensive simulation in which the dose was recalculated 600 times per given plan under the influence of random and systematic setup errors and proton range errors. On the basis of simulation results, we determined the probability of dose variations and calculated the expected values and standard deviations of dose–volume histograms. The uncertainties in dose were spatially visualized on the planning CT as a probability map of failure to target coverage or overdose of critical structures. Results: The expected value of target coverage under the uncertainties was consistently lower than that of the nominal value determined from the clinical target volume coverage without setup error or range uncertainty, with a mean difference of −1.1% (−0.9% for breath-hold), −0.3%, and −2.2% for lung, prostate, and a brain cases, respectively. The organs with most sensitive dose under uncertainties were esophagus and spinal cord for lung, rectum for prostate, and brain stem for brain cancer. Conclusions: A clinically feasible robustness plan analysis tool based on direct dose calculation and statistical simulation has been developed. Both the expectation value and standard deviation are useful to evaluate the impact of uncertainties. The existing proton beam planning method used in this institution seems to be adequate in terms of target coverage. However, structures that are small in volume or located near the target area showed greater sensitivity to uncertainties.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2010

Mitigation of Angle Tracking Errors Due to Color Dependent Centroid Shifts in SIM-Lite

The SIM-Lite astrometric interferometer will search for Earth-size planets in the habitable zones of nearby stars. In this search the interferometer will monitor the astrometric position of candidate stars relative to nearby reference stars over the course of a 5 year mission. The elemental measurement is the angle between a target star and a reference star. This is a two-step process, in which the interferometer will each time need to use its controllable optics to align the starlight in the two arms with each other and with the metrology beams. The sensor for this alignment is an angle tracking CCD camera. Various constraints in the design of the camera subject it to systematic alignment errors when observing a star of one spectrum compared with a start of a different spectrum. This effect is called a Color Dependent Centroid Shift (CDCS) and has been studied extensively with SIM-Lite's SCDU testbed. Here we describe results from the simulation and testing of this error in the SCDU testbed, as well as effective ways that it can be reduced to acceptable levels.

Spectral↗
DOE OSTI2014

A single-gradient junction technique to replace multiple-junction shifts for craniospinal irradiation treatment

Craniospinal irradiation (CSI) requires abutting fields at the cervical spine. Junction shifts are conventionally used to prevent setup error–induced overdosage/underdosage from occurring at the same location. This study compared the dosimetric differences at the cranial-spinal junction between a single-gradient junction technique and conventional multiple-junction shifts and evaluated the effect of setup errors on the dose distributions between both techniques for a treatment course and single fraction. Conventionally, 2 lateral brain fields and a posterior spine field(s) are used for CSI with weekly 1-cm junction shifts. We retrospectively replanned 4 CSI patients using a single-gradient junction between the lateral brain fields and the posterior spine field. The fields were extended to allow a minimum 3-cm field overlap. The dose gradient at the junction was achieved using dose painting and intensity-modulated radiation therapy planning. The effect of positioning setup errors on the dose distributions for both techniques was simulated by applying shifts of ± 3 and 5 mm. The resulting cervical spine doses across the field junction for both techniques were calculated and compared. Dose profiles were obtained for both a single fraction and entire treatment course to include the effects of the conventional weekly junction shifts. Compared with the conventional technique, the gradient-dose technique resulted in higher dose uniformity and conformity to the target volumes, lower organ at risk (OAR) mean and maximum doses, and diminished hot spots from systematic positioning errors over the course of treatment. Single-fraction hot and cold spots were improved for the gradient-dose technique. The single-gradient junction technique provides improved conformity, dose uniformity, diminished hot spots, lower OAR mean and maximum dose, and one plan for the entire treatment course, which reduces the potential human error associated with conventional 4-shifted plans.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1994

Toward unbiased determination of the redshift evolution of Lyman-alpha forest clouds

The possibility of using D(sub A), the mean depression of a quasar spectrum due to Ly-alpha forest absorption, to study the number density evolution of the Ly-alpha forest clouds is examined in some detail. Current D(sub A) measurements are made against a continuum that is a power-law extrapolation from the continuum longward of Ly-alpha emission. Compared to the line-counting approach, the D(sub A)-method has the advantage that the D(sub A) measurements are not affected by line-blending effects. However, we find using low-redshift quasar spectra obtained with the Hubble Space Telescope (HST), where the true continuum in the Ly-alpha forest can be estimated fairly reliably because of the much lower density of the Ly-alpha forest lines, that the extrapolated continuum often deviates systematically from the true continuum in the forest region. Such systematic continuum errors introduce large errors in the D(sub A) measurements. The current D(sub A) measurements may also be significantly biased by the possible presence of the Gunn-Peterson absorption. We propose a modification to the existing D(sub A)-method, namely, to measure D(sub A) against a locally established continuum in the Ly-alpha forest. Under conditions that the quasar spectrum has good resolution and S/N to allow for a reliable estimate of the local continuum in the Ly-alpha forest, the modified D(sub A) measurements should be largely free of the systematic uncertainties suffered by the existing D(sub A) measurements. We also introduce a formalism based on the work of Zuo (1993) to simplify the application of the D(sub A)-method(s) to real data. We discuss the merits and limitations of the modified D(sub A)-method, and conclude that it is a useful alternative. Our findings that the extrapolated continuum from longward of Ly-alpha emission often deviates systematically from the true continuum in the Ly-alpha forest present a major problem in the study of the Gunn-Peterson absorption.

Lu, Limin↗
DOE OSTI2012

TOWARD A DIRECT MEASUREMENT OF THE COSMIC ACCELERATION

We present precise H I 21 cm absorption line redshifts observed in multiple epochs to directly constrain the secular redshift drift z-dot or the cosmic acceleration, {Delta}v/{Delta}t{sub circle}. A comparison of literature analog spectra to contemporary digital spectra shows significant acceleration likely attributable to systematic instrumental errors. However, we obtain robust constraints using primarily Green Bank Telescope digital data. Ten objects spanning z = 0.09-0.69 observed over 13.5 years show z-dot = (-2.3 {+-} 0.8) Multiplication-Sign 10{sup -8} yr{sup -1} or {Delta}v/{Delta}t{sub circle} = -5.5 {+-} 2.2 m s{sup -1} yr{sup -1}. The best constraint from a single object, 3C 286 at (z) = 0.692153275(85), is z-dot = (1.6 {+-} 4.7) Multiplication-Sign 10{sup -8} yr{sup -1} or {Delta}v/{Delta}t{sub circle} = 2.8 {+-} 8.4 m s{sup -1} yr{sup -1}. These measurements are three orders of magnitude larger than the theoretically expected acceleration at z = 0.5, z-dot = 2 Multiplication-Sign 10{sup -11} yr{sup -1} or {Delta}v/{Delta}t{sub circle} = 0.3 cm s{sup -1} yr{sup -1}, but they demonstrate the lack of peculiar acceleration in absorption line systems and the long-term frequency stability of modern radio telescopes. A comparison of UV metal absorption lines to the 21 cm line improves constraints on the cosmic variation of physical constants: {Delta}({alpha}{sup 2} g{sub p} {mu})/{alpha}{sup 2} g{sub p} {mu} = (- 1.2 {+-} 1.4) Multiplication-Sign 10{sup -6} in the redshift range z = 0.24-2.04. The linear evolution over the last 10.4 Gyr is (- 0.2 {+-} 2.7) Multiplication-Sign 10{sup -16} yr{sup -1}, consistent with no variation. The cosmic acceleration could be directly measured in {approx}125 years using current telescopes or in {approx}5 years using a Square Kilometer Array, but systematic effects will arise at the 1 cm s{sup -1} yr{sup -1} level.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2024

Revised estimates of NO 2 reductions during the COVID-19 lockdowns using updated TROPOMI NO 2 retrievals and model simulations

The TROPOspheric Monitoring Instrument (TROPOMI) observed unprecedented declines in NO 2 vertical column densities (VCD) over the world's most densely populated cities during the 2020 COVID-19 lockdowns. These favorable changes in NO 2 air quality were correlated with sharp reductions in traffic volume and economic activity during the lockdowns. In this comprehensive global study, we provide revised estimates of the declines in anthropogenic emissions for 36 megacities using a novel methodology for disentangling the anthropogenic emissions from the meteorological transport and natural variability. We further quantify the uncertainty associated with changes in the a priori profile shape information during the lockdowns due to reduced emissions. Satellite NO 2 retrieval techniques calculate an air mass factor that requires a priori NO 2 profile shape information representative of the local atmosphere. This information, which is typically obtained from a chemical transport model (CTM), was not available for the early studies. This study also accounts for the satellite sampling errors resulting from the selective sampling of non-cloudy scenes during the study period. For our analysis, we used CTM simulations that were generated with and without COVID-impacted emissions. We perform retrievals of tropospheric NO 2 columns with the NASA NO 2 algorithm, and then use observed and simulated data to disentangle the meteorological transport from the contribution due anthropogenic emissions. We found that the meteorological transport was most significant source of variability ranging between −35% and 22% of the change total tropospheric VCD. We also find that not accounting for changes in the a priori NO 2 profile shape information during the lockdowns resulted in systematic retrieval errors that were up to 12% of the estimated decline, and the elimination of cloud contaminated scenes resulted in sampling errors that in general ranged between varied ±15%.

NO2↗
DOE OSTI2018

Introduction to CAUSES: Description of weather and climate models and their near-surface temperature errors in 5-day hindcasts near the Southern Great Plains

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen-temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from 9 institutes, perform a series of 5-day hindcasts, each initialised from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen-temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS1989

Raman intensity as a probe of concentration near a crystal growing in solution

The feasibility of using Raman spectral scattering signals for measurements of concentration profiles near a crystal interface during growth or dissolution is discussed. With KH2PO4 (KDP) as a test material, optical multichannel analyzer (OMA) detection of a solute Raman vibrational band provided direct quantification of solute concentration with band intensity. The intersection of incident laser and Raman collection optics provided 3-D selective point measurements of the solution concentration field. Unlike many other techniques, the Raman band intensity is not sensitive to the typical temperature variations. Precision calibration of Raman intensity versus KDP concentration with less than 1 pct standard deviation error levels was demonstrated. A fiber optic, which sampled incident laser intensity and coupled it to the OMA, provided a fully synchronized monitor of fluctuations in laser power to correlate with observed Raman signals. With 1 W of laser power at the sample, good data statistics required eight repeated data collections at approximately 2.5 min collection. The accumulated time represents the concentration measurement time at one spatial location. Photomicroscopy documented a 30 micrometer diameter by 200 micrometer of laser Raman scattering region in the solution near the crystal surface. The laser beam was able to approach up to 25 micrometer from the crystal surface. However, a crystal surface reflected intensity contribution was weakly detectable. Nucleated microcrystals were seen in the crystal-growing solution. These microcrystals convect right up to the crystal surface and indicate no quiet diffusion region under normal gravity conditions. Translation of the solution cell with respect to the optics caused systematic intensity errors.

Wilkinson, R. Allen↗
NASA NTRS2013

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Model Error↗
DOE OSTI2026

Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis

Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol. Across the model set, we observe clear scale-dependent trends in both baseline extraction quality and the effectiveness of reflective reasoning (self-checking) under our fixed-prompt, no-augmentation setting. Mid-scale models often provide a favorable balance of accuracy and efficiency, whereas the smallest models show limited or inconsistent gains from the reasoning variants tested. Larger models achieve the highest overall F1 scores but incur substantially greater compute and infrastructure requirements. We further find that reliability failure modes can distort conventional metrics in this domain: in particular, high recall can coincide with systematic extraction errors when models fabricate values for fields that are absent from the source text, underscoring the importance of conservative null handling and evidence-grounded evaluation. Overall, our study provides a reproducible resource–performance comparison for open-weight LLM-based extraction in hydropower regulatory documentation and offers practical guidance for model selection under different deployment constraints.

Evaluation protocol↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Design and expected performance of a fast neutron attenuation probe for light element density measurements

In this paper, we present the design and expected performance of a proof-of-concept 32 channel material identification system. Our system is based on the energy-dependent attenuation of fast neutrons for four elements: hydrogen, carbon, nitrogen and oxygen. We describe a new approach to obtaining a broad range of neutron energies to probe a sample, as well as our technique for reconstructing the molar densities within a sample. The system's performance as a function of time-of-flight energy resolution is explored using a Geant4-based Monte Carlo. Our results indicate that, with the expected detector response of our system, we will be able to determine the molar density of all four elements to within a 20–30% accuracy in a two hour scan time. In many cases this error is systematically low, thus the ratio between elements is more accurate. This degree of accuracy is enough to distinguish, for example, a sample of water from a sample of pure hydrogen peroxide: the ratio of oxygen to hydrogen is reconstructed to within 8±0.5% of the true value. Lastly, with future algorithm development that accounts for backgrounds caused by scattering within the sample itself, the accuracy of molar densities, not ratios, may improve to the 5–10% level for a two hour scan time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗