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DOE OSTI2023

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

DARK MATTER SEARCHES IN THE GAMMA-RAY EXTRAGALACTIC BACKGROUND VIA CROSS-CORRELATIONS WITH GALAXY CATALOGS

We compare the measured angular cross-correlation between the Fermi-Large Area Telescope γ-ray sky and catalogs of extragalactic objects with the expected signal induced by weakly interacting massive particle (WIMP) dark matter (DM). We include a detailed description of the contribution of astrophysical γ-ray emitters such as blazars, misaligned active galactic nucleus (AGN), and star-forming galaxies, and perform a global fit to the measured cross-correlation. Five catalogs are considered: Sloan Digital Sky Survey (SDSS)-DR6 quasars, Two Micron All Sky Survey galaxies, NRAO VLA Sky Survey radio galaxies, SDSS-DR8 Luminous Red Galaxies, and the SDSS-DR8 main galaxy sample. To model the cross-correlation signal, we use the halo occupation distribution formalism to estimate the number of galaxies of a given catalog in DM halos and their spatial correlation properties. We discuss uncertainties in the predicted cross-correlation signal arising from the DM clustering and WIMP microscopic properties, which set the DM γ-ray emission. The use of different catalogs probing objects at different redshifts significantly reduces, though not completely, the degeneracy among the different γ-ray components. We find that the presence of a significant WIMP DM signal is allowed by the data but not significantly preferred by the fit, although this is mainly due to a degeneracy with the misaligned AGN component. With modest substructure boost, the sensitivity of this method excludes thermal annihilation cross sections at 95% level for WIMP masses up to few tens of GeV. Constraining the low-redshift properties of astrophysical populations with future data will further improve the sensitivity to DM.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Search for Higgs Boson and Observation of $Z$ Boson through Their Decay into a Charm Quark-Antiquark Pair in Boosted Topologies in Proton-Proton Collisions at $\sqrt{s}$ = 13 TeV

A search for the standard model (SM) Higgs boson ($H$) produced with transverse momentum ($p_T$) greater than 450 GeV and decaying to a charm quark-antiquark ($c\bar{c}$) pair is presented. The search is performed using proton-proton collision data collected at $\sqrt{s} = 13$ TeV by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138 fb$^{-1}$. Boosted $H \to c\overline{c}$ decay products are reconstructed as a single large-radius jet and identified using a deep neural network charm tagging technique. The method is validated by measuring the $Z \to c\bar{c}$ decay process, which is observed in association with jets at high $p_T$ for the first time with a signal strength of $1.00^{+0.17}_{-0.14}$ (syst)$\pm 0.08$ (theo) $\pm 0.06$ (stat), defined as the ratio of the observed process rate to the SM expectation. The observed (expected) upper limit on $\sigma (H){\cal B}(H \to c\overline{c})$ is set at 47 (39) times the SM prediction at 95\%confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗

DOE OSTI2016

SU-F-J-131: Reproducibility of Positioning Error Due to Temporarily Indwelled Urethral Catheter for Urethra-Sparing Prostate IMRT

Purpose: The purpose of this study was to prospectively assess the reproducibility of positioning errors due to temporarily indwelled catheter in urethra-sparing image-guided (IG) IMRT. Methods: Ten patients received urethra-sparing prostate IG-IMRT with implanted fiducials. After the first CT scan was performed in supine position, 6-Fr catheter was indwelled into urethra, and the second CT images were taken for planning. While the PTV received 80 Gy, 5% dose reduction was applied for the urethral PRV along the catheter. Additional CT scans were also performed at 5th and 30th fraction. Positions of interests (POIs) were set on posterior edge of prostate at beam isocenter level (POI1) and cranial and caudal edge of prostatic urethra on the post-indwelled CT images. POIs were copied into the pre-indwelled, 5th and 30th fraction’s CT images after fiducial matching on these CT images. The deviation of each POI between pre- and post-indwelled CT and the reproducibility of prostate displacement due to catheter were evaluated. Results: The deviation of POI1 caused by the indwelled catheter to the directions of RL/AP/SI (mm) was 0.20±0.27/−0.64±2.43/1.02±2.31, respectively, and the absolute distances (mm) were 3.15±1.41. The deviation tends to be larger if closer to the caudal edge of prostate. Compared with the pre-indwelled CT scan, a median displacement of all POIs (mm) were 0.3±0.2/2.2±1.1/2.0±2.6 in the post-indwelled, 0.4±0.4/3.4±2.1/2.3±2.6 in 5th, and 0.5±0.5/1.7±2.2/1.9±3.1 in 30th fraction’s CT scan with a similar data distribution. There were 6 patients with 5-mm-over displacement in AP and/or CC directions. Conclusion: Reproducibility of positioning errors due to temporarily indwelling catheter was observed. Especially in case of patients with unusually large shifts by indwelling catheter at the planning process, treatment planning should be performed by using the pre-indwelled CT images with transferred contour of the urethra identified by post-indwelled CT images.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of radiotherapy, is therefore well-suited for a number of MRI-guided adaptive radiotherapy, and potentially enhance prostate radiotherapy treatment outcome.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

High throughput embryonic zebrafish test with automated dechorionation to evaluate nanomaterial toxicity

Engineered nanomaterials pose occupational health and environmental concerns as they possess unique physical and chemical properties that can contribute to toxicity. High throughput toxicity screening methods are needed to address the increasing number of nanomaterials in production. Here we used a zebrafish photomotor response (PMR) test to evaluate a set of fifteen nanomaterials with military relevance. Automated dechorionation of zebrafish embryos was used to enhance nanomaterials bioavailability. Optimal PMR activity in zebrafish embryos was found at 30–31 hours post-fertilization (hpf). Behavioral and toxicological responses were measured at 30 and 120 hpf; behavioral responses were found for thirteen of the fifteen nanomaterials and acute toxicity (LC50) levels for nine of the fifteen nanomaterials below the maximum test concentration of 500 μg/ml. Physico-chemical characterization of the nanomaterials detected endotoxin and bacterial contamination in two of the tested samples, which may have contributed to observed toxicity and reinforces the need for physical and chemical characterization of nanomaterials use in toxicity testing. The zebrafish PMR test, together with automated dechorionation, provides an initial rapid assessment of the behavioral effects and toxicity of engineered nanomaterials that can be followed up by physico-chemical characterization if toxicity is detected, reducing the amount of time and monetary constraints of physico-chemical testing.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Fermilab Booster loss modelling and rebalancing using Bayesian methods

Fermilab Booster is being upgraded for the PIP-II project to support 20Hz ramp rate at higher intensities. Loss trip limits determine the achievable peak power. To meet PIP-II requirements, losses need to be halved as compared to current levels. Losses primarily occur at injection and transition crossing, with both gradually increasing and threshold-like intensity-dependent behaviors. The existing simulation models are not yet good enough for quantitative loss predictions. In practice, it will be necessary to tune up the Booster using iterative methods and operator intuition. In this paper we present an effort to systematically model Booster losses using active learning (Bayesian exploration) techniques, and subsequently to rebalance them for higher trip limit margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. This is a complex task due to safety and timing requirements – we discuss mitigations such as uncertainty constraints and approximate fitting. Once models are stable, we perform large-scale single and multi-objective tuning using scalarized objectives made up of critical beam loss locations. Our results demonstrate significant rebalancing of losses, increasing trip margins, as well as an overall improvement in beam transmission efficiency. We are exploring how to combine existing simulations with experimental data and automate the collection procedure so that more advanced surrogate models can be created over time.

Kuklev, Nikita [Fermilab]↗

DOE OSTI2024

Circulating levels of micronutrients and risk of osteomyelitis: a Mendelian randomization study

Background Few observational studies have investigated the effect of micronutrients on osteomyelitis, and these findings are limited by confounding and conflicting results. Therefore, we conducted Mendelian randomization (MR) analyses to evaluate the association between blood levels of eight micronutrients (copper, selenium, zinc, vitamin B12, vitamin C, and vitamin D, vitamin B6, vitamin E) and the risk of osteomyelitis. Methods We performed the two-sample and multivariable Mendelian randomization (MVMR) to investigate causation, where instrument variables for the predictor (micronutrients) were derived from the summary data of micronutrients from independent cohorts of European ancestry. The outcome instrumental variables were used from the summary data of European-ancestry individuals ( n = 486,484). The threshold of statistical significance was set at p < 0.00625. Results We found a significant causal association that elevated zinc heightens the risk of developing osteomyelitis in European ancestry individuals OR = 1.23 [95% confidence interval (CI) [1.07, 1.43]; p = 4.26E-03]. Similarly, vitamin B6 showed a similar significant causal effect on osteomyelitis as a risk factor OR = 2.78 (95% CI [1.34, 5.76]; p = 6.04E-03; in the secondary analysis). Post-hoc analysis suggested this result (vitamin B6). However, the multivariable Mendelian randomization (MVMR) provides evidence against the causal association between zinc and osteomyelitis OR = 0.98(95% CI [−0.11, 0.07]; p = 7.20E-1). After searching in PhenoScanner, no SNP with confounding factors was found in the analysis of vitamin B6. There was no evidence of a reverse causal impact of osteomyelitis on zinc and vitamin B6. Conclusion This study supported a strong causal association between vitamin B6 and osteomyelitis while reporting a dubious causal association between zinc and osteomyelitis.

Zhang, Xu↗

DOE OSTI2018

Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C

A new temperature goal of “holding the increase in global average temperature well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels” has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0°C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106–2115 in warming scenarios of 1.5 and 2.0°C using a method of ensemble simulation with well-validated Model to capture the Crop–Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

New laser technique for the identification of molecular transitions.

A laser technique is proposed which may be useful for the assignment of molecular spectra in the visible and infrared regions. The method is based on the resonant interaction of two monochromatic fields with a Doppler-broadened three-level system. Under the appropriate conditions the absorption line shape of one of the transitions shows a complex structure over a narrow section of the Doppler profile, and for sufficiently high laser power the line shape splits into a number of narrow peaks. Analysis of the resulting intensity pattern leads to unambiguous assignment of the angular momentum quantum numbers of the three levels involved. A simple set of rules is given to facilitate interpretation of spectra. The line shapes discussed are also relevant to monochromatic optical pumping of gases and unidirectional laser amplifiers.

Skribanowitz, N.↗

NASA NTRS1977

Set-point control of nonlinear systems

A method of nonlinear stochastic control is applied to the problem of providing set-point control. The procedure is illustrated for a nonlinear second-order plant. The controller design uses a variation of the 'multiple-model adaptive control' approach. Specific problems related to the design are investigated, including learning time and set-point accuracy dependence on the number of approximating models, chord versus tangent approximations, and the a priori Markov switching parameter values. Simulations are provided for different situations under various levels of measurement noise.

Vanlandingham, H. F.↗

NASA NTRS1994

Automating the training development process for mission flight operations

Traditional methods of developing training do not effectively support the changing needs of operational users in a multimission environment. The Automated Training Development System (ATDS) provides advantages over conventional methods in quality, quantity, turnaround, database maintenance, and focus on individualized instruction. The Operations System Training Group at the JPL performed a six-month study to assess the potential of ATDS to automate curriculum development and to generate and maintain course materials. To begin the study, the group acquired readily available hardware and participated in a two-week training session to introduce the process. ATDS is a building activity that combines training's traditional information-gathering with a hierarchical method for interleaving the elements. The program can be described fairly simply. A comprehensive list of candidate tasks determines the content of the database; from that database, selected critical tasks dictate which competencies of skill and knowledge to include in course material for the target audience. The training developer adds pertinent planning information about each task to the database, then ATDS generates a tailored set of instructional material, based on the specific set of selection criteria. Course material consistently leads students to a prescribed level of competency.

Scott, Carol J.↗

NASA NTRS2020

Neural Network Repair of Lossy Compression Artifacts in the Sept 2015 – March 2016 Duration of the MMS/FPI Dataset

During the Sept 2015 –March 2016 duration (sometimes referred to as Phase 1A) of the Magnetospheric Multiscale Mission (MMS), the Dual Electron Spectrometers (DES) were configured to generously utilize lossy compression. While this maximized the number of velocity distribution functions downlinked, it cameat the expense of lost information content for a fractionof the frames. Following this period of lossy compression, the DES was re-configured in a way that allowed for 95% of the framesto arrive to the ground without loss. Using this high-quality set offrameson-orbit observations, we compressed and decompressed the frameson the ground to create a side-by-side record of the compression effect. This record was used to drive an optimization method that (a) derived basis functions capable of approximating the lossless sample space and with non-negative coefficients and (b) fitted a function which maps the lossy framesto basis weights that recreate the framewithout compression artifacts. This methodis introduced and evaluated in this paper.Data users should expect a higher level of confidence in the absolute scale of density/temperature measurements andnotice less sinusoidal bias in the velocity X and Y components(GSE).

Magnetosphere, Neural Networks, Image Processing, ↗

DOE OSTI2023

Search for $ {\textrm{B}}_{\textrm{s}}^0 $ → ℓ ∓ τ ± with the semi-leptonic tagging method at Belle

We present a search for the lepton-flavor-violating decays ${B}_s^0$ → ℓ ∓ τ ± , where ℓ = e, μ, using the full data sample of 121 fb -1 collected at the Υ(5S) resonance with the Belle detector at the KEKB asymmetric-energy e + e - collider. We use ${B}_s^0{\overline{B}}_s^0$ events in which one ${B}_s^0$ meson is reconstructed in a semileptonic decay mode and the other in the signal mode. We find no evidence for (${B}_s^0$ → ℓ ∓ τ ± decays and set upper limits on their branching fractions at 90% confidence level as $\mathcal{B}$(${B}_s^0$ → e ∓ τ ± ) < 14 × 10 -4 and $\mathcal{B}$ (${B}_s^0$ → μ ∓ τ ± ) < 7.3 × 10 -4 . Our result represents the first upper limit on the ${B}_s^0$ → e ∓ τ ± decay rate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

rascalc : a jackknife approach to estimating single- and multitracer galaxy covariance matrices

ABSTRACT To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer data sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

TU-CD-207-11: Patient-Driven Automatic Exposure Control for Dedicated Breast CT

Purpose: To implement automatic exposure control (AEC) in dedicated breast CT (bCT) on a patient-specific basis using only the pre-scan scout views. Methods: Using a large cohort (N=153) of bCT data sets, the breast effective diameter (D) and width in orthogonal planes (Wa,Wb) were calculated from the reconstructed bCT image and pre-scan scout views, respectively. D, Wa, and Wb were measured at the breast center-of-mass (COM), making use of the known geometry of our bCT system. These data were then fit to a second-order polynomial “D=F(Wa,Wb)” in a least squares sense in order to provide a functional form for determining the breast diameter. The coefficient of determination (R{sup 2}) and mean percent error between the measured breast diameter and fit breast diameter were used to evaluate the overall robustness of the polynomial fit. Lastly, previously-reported bCT technique factors derived from Monte Carlo simulations were used to determine the tube current required for each breast diameter in order to match two-view mammographic dose levels. Results: F(Wa,Wb) provided fitted breast diameters in agreement with the measured breast diameters resulting in R{sup 2} values ranging from 0.908 to 0.929 and mean percent errors ranging from 3.2% to 3.7%. For all 153 bCT data sets used in this study, the fitted breast diameters ranged from 7.9 cm to 15.7 cm corresponding to tube current values ranging from 0.6 mA to 4.9 mA in order to deliver the same dose as two-view mammography in a 50% glandular breast with a 80 kV x-ray beam and 16.6 second scan time. Conclusion: The present work provides a robust framework for AEC in dedicated bCT using only the width measurements derived from the two orthogonal pre-scan scout views. Future work will investigate how these automatically chosen exposure levels affect the quality of the reconstructed image.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗