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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Interpolating Low Time-Resolution Forecast Data

Methodology that interpolates low time-resolution data (e.g., hourly) to high time-resolution (e.g., minutely) with variability patterns extracted from historical records. Magnitude of the variability inserted into the low time resolution data can be adjusted according to the installed capacity represented by the low time-resolution data compared to that by historical records. This approach enables detailed analysis of the impacts from wind and solar on power system intra-hour operations and balancing reserve requirements even with only hourly data. It also allows convenient creation of high resolution wind or solar generation data with various degree of variability to investigate their operational impacts. The methodology comprises of the following steps: 1. Smooth the historical data (set A) with an appropriate window length l to get its trend (set B); l can be a fraction of an hour (e.g., 15 minutes) or longer than an hour, of which the length of the variability patterns will be; 2. Extract the variable component (set C) of historical data by subtracting the smooth trend from it, i.e. set C = set A - set B 3. For each window length l of the variable component data set, find the average value x (will call it base component) of the corresponding window of the historical data set; 4. Define a series of segments (set D) that the values of data will be grouped into, e.g. (0, 0.1), (0.1, 0.2), ..., (0.9, 1.0) after normalization; Link each variability pattern to a data segment based on its corresponding base component x; after this step, each data segment should be linked to multiple variability patterns after this step; 5. Use spline function to interpolate the low time-resolution forecast data (set E) to become a high time-resolution smooth curve (set F); 6. Based on the window length l , calculate the average value y in each window length of set F; find the data segment that y belongs to; then randomly select one of the variability patterns linked to this data segment; 7. Add the selected variability pattern in each window to set F to get the high time-resolution data (set G) with variability observed from historical data set A; a coefficient k can be multiplied to the selected variability pattern to reflect the potential difference in variability between forecast set E and historical data set A caused by installation capacity and other factors. 8. After set G is constructed, variability statistics can be checked for set A and set G for comparison. Adjust the coefficient k if necessary to achieve desired variability in the reconstructed data.

Lu, Shuai↗
DOE OSTI2018

Experimental Technetium Release from Simulated Salt-stone Cementitious Waste Form - 18419

Experiments are being conducted to evaluate the early release of technetium (Tc) from fresh salt-stone waste forms using dynamic flow-through tests. Salt-stone is a chemically reducing, grout-based waste form used by the U.S. Department of Energy (DOE) to dispose of low-activity salt solutions arising from high-level radioactive waste tank closure activities at the Savannah River Site (SRS). A key safety function of salt-stone is maintaining a low-flow, chemically reducing environment that will attenuate the release of redox-sensitive radioelements such as Tc. Salt-stone also provides a physical barrier due to its low intrinsic permeability, which limits both water and oxygen ingress. These release experiments, conducted to support the U.S. Nuclear Regulatory Commission's monitoring role during tank closure, are designed to provide independent information on how well salt-stone will perform its safety function. The dynamic leach tests were conducted in a Hassler-type core-holder permeameter system that allows an accelerated flow rate of simulated SRS groundwater through an intact cylinder of salt-stone spiked with Tc-99 at a concentration of approximately 2,100 Bq/g (3 ppm). The first salt-stone specimen to be tested (Specimen 2) was prepared with Grade 100 ground granulated blast-furnace slag and aged for 119 days in a temperature-controlled, low-oxygen, high-humidity environment. Before test completion, 9.5 pore volumes (PV) of simulated SRS groundwater were passed through the salt-stone cylinder. After a few PV, apparent saturated hydraulic conductivity (K{sub sat}) values calculated from flow data reached a steady-state value of approximately 1.0 x 10{sup -9} cm/s, which is within the range of previously reported data. Tc-99 concentrations in leachate peaked early during the leach test at 2.0 x 10{sup -7} M and then declined to approximately 4 x 10{sup -8} M after 9.5 PV. The leachate Tc-99 concentration after elution of 9.5 PV is broadly consistent with a Tc solution concentration controlled by the solubility of reduced Tc solids. The range of Tc-99 concentrations is similar to the range reported for a column test that was sponsored by DOE, but the time pattern of release differs. The second salt-stone specimen tested (Specimen 3) was aged for 372 days and it further differed from the first specimen in that its Grade 100 slag component had been finely ground using a mortar and pestle in an attempt to make it more reactive. This second test yielded Tc release behavior broadly similar to that observed during the first test, except that the early Tc peak (at 1.2 x 10{sup -6} M) was six times higher and the concentration after 6.7 PV (8.8 x 10{sup -8} M) was approximately two times higher than in the first test. In addition, the steady-state K{sub sat} was an order of magnitude lower than that of the first specimen. During the second test, the injection pump was intentionally turned off for a period of 11 days (i.e., approximate residence time for 1 PV) and a Tc spike was observed when pumping resumed, suggesting that longer residence times may promote higher Tc effluent concentrations. During a second intentional shut-down of the pump for 25 days (approximate residence time for 2 PV), another Tc spike was observed. An unplanned excursion to higher confining pressure physically affected the specimen, temporarily lowering the apparent K{sub sat} followed by a subsequent increase in both K{sub sat} and Tc effluent concentration. One hypothesis is that the transient spike in confining pressure and resulting salt-stone compression and later decompression may have induced Tc release from otherwise inactive pores. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1984

Study on spectral/radiometric characteristics of the Thematic Mapper for land use applications

An information theoretic measure of multispectral information content is developed and applied to a simultaneous LANDSAT TM and MSS data set. The entropy based function measures the dispersion and concentration of signal values in various data spaces, irrespective of specific class memberships. It is used to compare the information content of TM and MSS and of various subsets of TM and MSS bans, as well as tasseled cap transformations of the band values. Differences exist between the information measure results and results using variance based measures. System design information capacities and data space volumes are also compared. The results and observations presented are considered preliminary in nature since only one real and one simulated data set are analyzed.

Malila, W. A.↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

CCN Measurements at Urban Site during CoURAGE

Cloud condensation nuclei (CCN) measurements were collected during CoURAGE from March - June 2025 at the BSEC urban atmospheric chemistry supersite (39.32057 N, 76.6246 W). CCN number concentrations (# cm-3) were obtained at a frequency of 1 second using a cloud condensation nuclei counter (CCN-100, Droplet Measurement Technologies). The CCN counter was operated in stepping mode, measuring CCN over a range of supersaturations from 0.2 to 1.0%. The settings of the instrument were set such that the supersaturation increased in increments of 0.2% up to the maximum supersaturation, and then decreased in increments of 0.2% down to the minimum supersaturation in a repeating cycle. The supersaturation of the instrument was calibrated weekly using ammonium sulfate. The weekly calibrated supersaturation values have been applied to the data. Data during transitions between supersaturation values were removed and replaced with a value of -999. Transitions are defined as the first 210 seconds after a new supersaturation value to ensure the instrument has stabilized following a change in supersaturation. Data were also flagged with a value of -999 if there were concerns with the function of the instrument, specifically the presence of alarm codes, fogging of the optical particle counter (1st stage monitor > 0.4 V), or a sheath to sample flow ratio outside the expected range (< 9 or > 11).

CCN number density↗
DOE OSTI2014

Diamonds in the rough: a strong case for the inclusion of weak-intensity X-ray diffraction data

Here, new evidence is provided to show that the inclusion of weak-intensity, high-resolution X-ray diffraction data helps to improve the quality of experimental phases by imposing proper constraints on electron-density models during noncrystallographic symmetry averaging. Overwhelming evidence exists to show that the inclusion of weak-intensity, high-resolution X-ray diffraction data helps improve the refinement of atomic models by imposing strong constraints on individual and overall temperature B factors and thus the quality of crystal structures. Some researchers consider these data to be of little value and opt to discard them during data processing, particularly at medium and low resolution, at which individual B factors of atomic models cannot be refined. Here, new evidence is provided to show that the inclusion of these data helps to improve the quality of experimental phases by imposing proper constraints on electron-density models during noncrystallographic symmetry (NCS) averaging. Using electron-density correlation coefficients as criteria, the resolution of data has successfully been extended from 3.1 to 2.5 Å resolution with redundancy-independent merging R factors from below 100% to about 310%. It is further demonstrated that phase information can be fully extracted from observed amplitudes through de novo NCS averaging. Averaging starts with uniform density inside double-shelled spherical masks and NCS matrices that are derived from bound heavy-atom clusters at the vertices of cuboctahedrally symmetric protein particles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Assessment of New Approaches in Geothermal Exploration Decision Making: Preprint

Geothermal exploration projects have significant amount of risk associated with uncertainties encountered in the discovery of the geothermal resource. Understanding when and how to proceed in an exploration program, and when to walk away from a site, are two of the largest challenges for increased geothermal deployment. Current methodologies for exploration decision making is left to subjective by subjective expert opinion which can be incorrectly biased by expertise (e.g. geochemistry, geophysics), geographic location of focus, and the assumed conceptual model. The aim of this project is to develop a methodology for more objective geothermal exploration decision making at a given location, including go-no-go decision points to help developers and investors decide when to give up on a location. In this scope, two different approaches are investigated: 1) value of information analysis (VOIA) which is used for evaluating and quantifying the value of a data before they are purchased, and 2) enthalpy-based exploration targeting based on reservoir size, temperature gradient estimates, and internal rate of return (IRR). The first approach, VOIA, aims to identify the value of a particular data when making decisions with an uncertain outcome. This approach targets the pre-drilling phase of exploration. These estimated VOIs are highly affected by the size of the project and still have a high degree of subjectivity in assignment of probabilities. The second approach, exploration targeting, is focused on decision making during the drilling phase. It starts with a basic geothermal project definition that includes target and minimum required production capacity and initial budgeting for exploration phases. Then, it uses average temperature gradient, reservoir temperature estimates, and production capacity to define targets and go/no-go limits. The decision analysis in this approach is based on achieving a minimum IRR at each phase of the project. This second approach was determined to be less subjective, since it requires less subjectivity in the input values.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Assessment of New Approaches in Geothermal Exploration Decision Making

Geothermal exploration projects have significant amount of risk associated with uncertainties encountered in the discovery of the geothermal resource. Understanding when and how to proceed in an exploration program, and when to walk away from a site, are two of the largest challenges for increased geothermal deployment. Current methodologies for exploration decision making is left to subjective by subjective expert opinion which can be incorrectly biased by expertise (e.g. geochemistry, geophysics), geographic location of focus, and the assumed conceptual model. The aim of this project is to develop a methodology for more objective geothermal exploration decision making at a given location, including go-no-go decision points to help developers and investors decide when to give up on a location. In this scope, two different approaches are investigated: 1) value of information analysis (VOIA) which is used for evaluating and quantifying the value of a data before they are purchased, and 2) enthalpy-based exploration targeting based on reservoir size, temperature gradient estimates, and internal rate of return (IRR). The first approach, VOIA, aims to identify the value of a particular data when making decisions with an uncertain outcome. This approach targets the pre-drilling phase of exploration. These estimated VOIs are highly affected by the size of the project and still have a high degree of subjectivity in assignment of probabilities. The second approach, exploration targeting, is focused on decision making during the drilling phase. It starts with a basic geothermal project definition that includes target and minimum required production capacity and initial budgeting for exploration phases. Then, it uses average temperature gradient, reservoir temperature estimates, and production capacity to define targets and go/no-go limits. The decision analysis in this approach is based on achieving a minimum IRR at each phase of the project. This second approach was determined to be less subjective, since it requires less subjectivity in the input values.

15 GEOTHERMAL ENERGY↗
NASA NTRS1981

MAGSAT anomaly profiles of the eastern Indian Ocean

Ground tracks from SEASAT were used in an effort to develop qualititative relationships between the gravity field and MAGSAT magnetic anomalies in the eastern Indian Ocean. Investigation of data quality led to analyses of the average value (over 80 vector data points, or approximately 36 km intervals) and of the standard deviation of this average, as a mean of identifying noisy portions of the data. It was discovered that the plots of the average value minus the individual (measured) point value are most useful for identifying noisy areas and data spikes. Spectrum analysis using edited (spikes removed) data show that the noise floor is less than 1 nT and the slope of the spectrum in the region of wavelengths between 1200 km and 250 km is approximately -3. Consequently the estimated resolution limit improved from approximately 360 km to approximately 250 km.

Sailor, R. V.↗
DOE OSTI2018

SPRUCE Small Differences in Ombrotrophy Control Regional-Scale Variation in Methane Cycling among Sphagnum-Dominated Peatlands: Supporting Data

This dataset contains net ecosystem respiration and the isotopic composition of emitted methane (CH4) form flux chambers as well as anions and cations from ion chromatography. Samples were collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA from June 2013 to June 2014 (2013-06-01 to 2014-06-30). This dataset were used in analyses in Zalman et al. (2018), which reported on the mechanisms controlling carbon (C) cycling rates and greenhouse gas emissions using a combination of biogeochemical and microbial approaches in three low pH, Sphagnum-dominated peatlands in northern Minnesota. In particular, biogeochemical parameters (CH4 and carbon dioxide (CO2) production potentials, porewater and gas CH4 and CO2 concentrations, and associated isotopic signatures), soil chemistry (organic acids and phenolics), and microbial community characteristics (quantification of methanogen and methanotroph abundance and activity) over a 1-year period in these three peatlands. The objective of this intensive study was to analyze biogeochemical and microbial characteristics of three outwardly similar low pH, Sphagnum-dominated peatlands to determine key characteristics that could explain differences in anaerobic C cycling and net CH4 flux despite their close geographical proximity and similar soil pH and plant communities. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format. All figures from Zalman et al. (2018) and all additional online resources are provided for completeness in the user guide. During the August 2026 update to this dataset, the ‘Sampling_Date’ column in the data files was changed from MM/DD/YYYY to YYYY-MM-DD format. No data values were altered

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Controlling excited-state contamination in nucleon matrix elements

We present a detailed analysis of methods to reduce statistical errors and excited-state contamination in the calculation of matrix elements of quark bilinear operators in nucleon states. All the calculations were done on a 2+1-flavor ensemble with lattices of size 32 3 × 64 generated using the rational hybrid Monte Carlo algorithm at 𝑎 = 0.081 fm and with 𝑀 𝜋 = 312 MeV. The statistical precision of the data is improved using the all-mode-averaging method. We compare two methods for reducing excited-state contamination: a variational analysis and a 2-state fit to data at multiple values of the source-sink separation 𝑡 sep . We show that both methods can be tuned to significantly reduce excited-state contamination and discuss their relative advantages and cost effectiveness. Here a detailed analysis of the size of source smearing used in the calculation of quark propagators and the range of values of 𝑡 sep needed to demonstrate convergence of the isovector charges of the nucleon to the 𝑡 sep → ∞ estimates is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

Characteristics of maximum-value composite images from temporal AVHRR data

Red and near-infrared satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics. The technique minimizes cloud contamination, reduces directional reflectance and off-nadir viewing effects, minimizes sun-angle and shadow effects, and minimizes aerosol and water-vapor effects. The improvement is highly dependent on the state of the atmosphere, surface-cover type, and the viewing and illumination geometry of the sun, target and sensor. An example from southern Africa showed an increase of 40 percent from individual image values tothe final composite image. Limitations associated with the technique are discussed, and recommendations are given to improve this approach.

Holben, Brent N.↗
DOE OSTI2015

Probing cis-trans isomerization in the S{sub 1} state of C{sub 2}H{sub 2} via H-atom action and hot band-pumped IR-UV double resonance spectroscopies

We report novel experimental strategies that should prove instrumental in extending the vibrational and rotational assignments of the S{sub 1} state of acetylene, C{sub 2}H{sub 2}, in the region of the cis-trans isomerization barrier. At present, the assignments are essentially complete up to ∼500 cm{sup −1} below the barrier. Two difficulties arise when the assignments are continued to higher energies. One is that predissociation into C{sub 2}H + H sets in roughly 1100 cm{sup −1} below the barrier; the resulting quenching of laser-induced fluorescence (LIF) reduces its value for recording spectra in this region. The other difficulty is that tunneling through the barrier causes a staggering in the K-rotational structure of isomerizing vibrational levels. The assignment of these levels requires data for K values up to at least 3. Given the rotational selection rule K′ − ℓ{sup ′′} = ± 1, such data must be obtained via excited vibrational levels of the ground state with ℓ{sup ′′} > 0. In this paper, high resolution H-atom resonance-enhanced multiphoton ionization spectra are demonstrated to contain predissociated bands which are almost invisible in LIF spectra, while preliminary data using a hyperthermal pulsed nozzle show that ℓ{sup ′′} = 2 states can be selectively populated in a jet, giving access to K′ = 3 states in IR-UV double resonance.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

Comments on extracting the resonance strength parameter from yield data

The F(α,n) reaction is the focus of on-going research in part because it is an important source of neutrons in the nuclear fuel cycle which can be exploited to assay nuclear materials, especially uranium in the form of UF 6 . At the present time there remains some considerable uncertainty (of the order of ± 20%) in the thick target integrated over angle (α,n) yield from 19 F (100% natural abundance) and its compounds as discussed. An important thin target cross-section measurement is that of Wrean and Kavanagh who explore the region from below threshold (2.36 MeV) to approximately 3.1 MeV with fine energy resolution. Integration of their cross-section data over the slowing down history of a stopping α-particle allows the thick target yield to be calculated for incident energies up to 3.1 MeV. This trend can then be combined with data from other sources to obtain a thick target yield curve over the wider range of interest to the fuel cycle (roughly threshold to 10 MeV to include all relevant α-emitters). To estimate the thickness of the CaF 2 target they used, Wrean and Kavanagh separately measured the integrated yield of the 6.129 MeV γ-rays from the resonance at 340.5 keV (laboratory α-particle kinetic energy) in the 19 F(p,αγ) reaction. To interpret the data they adopted a resonance strength parameter of (22.3 ± 0.8) eV based on a determination by Becker et al. The value and its uncertainty directly affects the thickness estimate and the extracted (α,n) cross-section values. In their citation to Becker et al's work, Wrean and Kavanagh comment that they did not make use of an alternative value of (23.7±1.0) eV reported by Croft because they were unable to reproduce the value from the data given in that paper. The value they calculated for the resonance strength from the thick target yield given by Croft was 21.4 eV. The purpose of this communication is to revisit the paper by Croft published in this journal and specifically to explain the origin of the reported resonance strength. Fortunately the original notes spanning the period 12 January 1988 to 16 January 1990 were available to consult. Finally, in hindsight there is certainly a case of excessive brevity to rectify. In essence the step requiring explanation is how to compute the resonance strength, ω γ , from the reported thick target resonance yield Y.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Technical Note: On the calculation of stopping-power ratio for stoichiometric calibration in proton therapy

Purpose: The quantitative effects of assumptions made in the calculation of stopping-power ratios (SPRs) are investigated, for stoichiometric CT calibration in proton therapy. The assumptions investigated include the use of the Bethe formula without correction terms, Bragg additivity, the choice of I-value for water, and the data source for elemental I-values. Methods: The predictions of the Bethe formula for SPR (no correction terms) were validated against more sophisticated calculations using the SRIM software package for 72 human tissues. A stoichiometric calibration was then performed at our hospital. SPR was calculated for the human tissues using either the assumption of simple Bragg additivity or the Seltzer-Berger rule (as used in ICRU Reports 37 and 49). In each case, the calculation was performed twice: First, by assuming the I-value of water was an experimentally based value of 78 eV (value proposed in Errata and Addenda for ICRU Report 73) and second, by recalculating the I-value theoretically. The discrepancy between predictions using ICRU elemental I-values and the commonly used tables of Janni was also investigated. Results: Errors due to neglecting the correction terms to the Bethe formula were calculated at less than 0.1% for biological tissues. Discrepancies greater than 1%, however, were estimated due to departures from simple Bragg additivity when a fixed I-value for water was imposed. When the I-value for water was calculated in a consistent manner to that for tissue, this disagreement was substantially reduced. The difference between SPR predictions when using Janni’s or ICRU tables for I-values was up to 1.6%. Experimental data used for materials of relevance to proton therapy suggest that the ICRU-derived values provide somewhat more accurate results (root-mean-square-error: 0.8% versus 1.6%). Conclusions: The conclusions from this study are that (1) the Bethe formula can be safely used for SPR calculations without correction terms; (2) simple Bragg additivity can be reasonably assumed for compound materials; (3) if simple Bragg additivity is assumed, then the I-value for water should be calculated in a consistent manner to that of the tissue of interest (rather than using an experimentally derived value); (4) the ICRU Report 37 I-values may provide a better agreement with experiment than Janni’s tables.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Cosmic Voids in the SDSS DR12 BOSS Galaxy Sample: the Alcock–Paczyński test

We apply the Alcock–Paczyński (AP) test to stacked voids identified using the final data release (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS). We also use 1000 mock galaxy catalogs that match the geometry, density, and clustering properties of the BOSS sample in order to characterize the statistical uncertainties of our measurements and take into account systematic errors such as redshift space distortions. For both BOSS data and mock catalogs, we use the ZOBOV algorithm to identify voids, we stack together all voids with effective radii of 30--100 h{sup −1} Mpc in the redshift range of 0.43–0.7, and we accurately measure the shape of the stacked voids. Our tests with the mock catalogs show that we measure the stacked void ellipticity with a statistical precision of 2.6%. The stacked voids in redshift space are slightly squashed along the line of sight, consistent with previous studies. We repeat this measurement of stacked void shape in the BOSS data, assuming several values of Ω{sub m} within the flat ΛCDM model, and we compare this to the mock catalogs in redshift space to perform the AP test. We obtain a constraint of Ω{sub m}=0.38{sub −0.15}{sup +0.18} at the 68% confidence level from the AP test. We discuss the sources of statistical and systematic noise that affect the constraining power of this method. In particular, we find that the measured ellipticity of stacked voids changes more weakly with cosmology than the standard AP prediction, leading to significantly weaker constraints. We discuss how constraints will improve in future surveys with larger volumes and densities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Daily, 30 m Resolution NDSI Data for the East River Watershed, CO for 2000-2020

This dataset contains daily Normalized Difference Snow Index (NDSI) values at 30 m spatial resolution for the East River watershed in Colorado, USA. The temporal range of these data includes water years 2001-2020. These data were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). This model fuses low spatial and high temporal resolution data from MODIS (500 m, daily) with high spatial and low temporal resolution data from Landsat (30 m, 16 days) to create a 30m synthetic daily snow product. This product allows for the analysis of historical snow covered area trends in the East River Watershed at fine spatiotemporal resolutions where it was not available previously. This research was performed as a part of the Department of Energy’s Subsurface Biogeochemical Research Program with the primary intent of better understanding the timing and spatial patterns of water delivery to the Critical Zone in mountain watersheds. Each .zip file contains one "water year" of data (October 1 - September 30; i.e., water year 2010 starts October 1, 2010 and ends September 30, 2011). Each zip file contains the following: STARFM daily Normalized Difference Snow Index (NDSI) fusion data files in GeoTiff format with one layer for each day between Landsat data acquisition dates (i.e., for dates of Landsat acquisition, the Landsat image is included for that date). The study area is located in an area of Landsat path overlap, so Landsat dates acquisitions are every 7-9 days. Landsat NDSI files containing the high spatial (30m), low temporal (7-9 days due to Landsat path overlap) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Dates for which no Landsat data were obtained are included as NoData layers. MODIS NDSI files containing the high temporal (daily), low spatial (500m) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Please note the MODIS data were resampled to 30m pixels for input into the STARFM model. The data have a scale factor of 10,000 and a no data value of -32767. The projection of all datasets is WGS 84 (EPSG: 4326), which has a latitude/longitude based degree resolution of 0.0002694946 X 0.0002694946, and approximates to the 30 m spatial resolution mentioned above. The Layer Index files in .csv format. They contain information for each layer in the above GeoTiff files regarding the corresponding date for each layer, the fraction of pixels in the image that contain valid data (missing data is due to either cloud cover or poor data quality; these values are not percent snow cover). Dates of Landsat overpass are indicated in these files. If no Landsat data were able to be obtained due to cloud cover or lack of Landsat Tier 1 data available on Google Earth Engine, this is also noted.

EARTH SCIENCE > CRYOSPHERE > SNOW/ICE↗
DOE OSTI2019

Calculations of energies, transition rates, and lifetimes for the fluorine-like isoelectronic sequence with Z=31−35

Employing two state-of-the-art methods, multiconfiguration Dirac–Hartree–Fock and second-order many-body perturbation theory, highly accurate calculations are performed for the lowest 200 fine-structure levels arising from the 2l{sup 7},2l{sup 6}3l{sup ′} and 2l{sup 6}4l{sup ′} configurations of the fluorine-like isoelectronic sequence with Z=31−35. Complete and consistent atomic data, including excitation energies, lifetimes, wavelengths, and E1, E2, M1, M2 line strengths, oscillator strengths, and transition rates among these 200 levels are provided. Comparisons are made between the present two data sets, as well as with other available experimental and theoretical values. The present data are accurate enough for identification and deblending of emission lines involving the n=3,4 levels, and are also useful for modeling and diagnosing fusion plasmas.

74 ATOMIC AND MOLECULAR PHYSICS↗