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DOE OSTI2020

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO 2 , ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm 2 for the ARMP and 0.26 g/cm 2 for the AERONET datasets, with bias of 0.072 g/cm 2 and 0.096 g/cm 2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions

Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify the confidence of model inferences. However, chained models pose an additional challenge for uncertainty quantification, especially when input uncertainties need to be propagated. In such cases, a fully uncertainty-aware chain of models is required, where each step accepts a probability distribution over the input space, and produces a probability distribution over the output space. In this work, we present a case study for adapting a single model within an existing chain, designed for reconstruction within neutrino-Argon interactions, developed for neutrino oscillation experiments such as MicroBooNE, ICARUS, and the future DUNE experiment. We test the performance of an input uncertainty-enabled model against an uncertainty-blinded model using a method for generating synthetic noise. By comparing these two, we assess the increase in inference quality achieved by exposing models to upstream uncertainty estimates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius norm

In this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an unknown direction by the Frobenius norm of the difference (FND) to find the unknown direction. To generalize this concept, we develop a continuous Frobenius norm of the difference (CFND) as a continuous analog of the FND and derive its analytical expression. By relating fitted and normalized 2D Gaussian distributions, we show that the CFND approximates the FND, and we validate this relationship with computer simulations. We find that a first-order approximation of the CFND between two similar Gaussian distributions takes the form of an absolute sine function, offering a simple analytical form with potential for specialized applications in segmented inverse beta decay (IBD) neutrino detectors, astronomy, machine learning, and more. Although this method may easily extend to 3D scalar fields, our focus here is on 2D real-valued fields as it directly applies to directionality. Our methodology consists of modeling a 2D Gaussian distribution, binning the data into a histogram, and encoding it as a square matrix. Rotating this matrix around its geometric center and comparing it to a measured dataset using the FND gives us rotational data that we fit with an absolute sine function. The location of the minimum of this fit is the angle closest to the true angle of the direction in the measured dataset. We present the derivation and discuss initial applications of the CFND in our novel algorithm, demonstrating its success in approximating directionality in 2D distributions.

Data Analysis, Statistics and Probability (physics↗

DOE OSTI2025

Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale

Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Characterizing Dislocation Defects in Dark Field X-ray Microscopy Images of Bulk Aluminum

Material defects play a large role in material response under shock loading, yet the process of how these defects initiate, propagate, and annihilate is not well understood. Using a newly developed diagnostic, dark field X-ray microscopy (DFXM), we can now visualize the behavior of dislocation defects in materials at the mesoscale under varying conditions. Using data from the European Synchrotron Radiation Facility, we apply a variety of image processing techniques to capture relevant features to locate and characterize size and orientation of dislocation defects in bulk aluminum DFXM images. Beyond simple visualization, this analysis drives statistical characterization of the defects and their dynamic response to further improve the relevant physics models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

System, algorithm, and method using short pulse interrogation with neutrons to detect and identify matter

A system, method, and algorithm for detecting and identifying special nuclear material (SNM) (including fissionable material), explosives, or drugs is disclosed. This material detection system relies on active interrogation of material using a short, intense neutron pulse, and characterization of the resulting prompt gamma response in two short time windows, the first concurrent with and the second immediately following the end of the neutron pulse, optionally subject to a total transit delay time of the neutrons from the neutron source to the target and from the target to the gamma detector. A high data rate analysis system implements data stream analysis and statistical correlations of the two short time window gamma responses to rapidly detect or identify SNM, explosives, or drugs. The duration of the neutron pulse is so short that minimal dose results. Various shields help to minimize the background signal falling on the gamma detector to improve sensitivity.

Derzon, Mark S.↗

DOE OSTI2022

Assessment of nuclear data needs for advanced reactor demonstrations: application to the molten chloride reactor experiment (MCRE)

A number of advanced reactor concepts are planned for near-term demonstrations including micro-reactors, larger demonstrations and space nuclear systems. These reactor concepts are based on a wide variety of reactor technologies, including sodium, gas, and salt cooling. An overlooked area in the development and ultimate startup of these reactors is addressing nuclear data needs that allow confident prediction of the criticality, safety requirements, and operation of the reactors. In this paper we try to address the problem of assessing nuclear data needs and possible remedies to reduce the existing uncertainties for advanced nuclear reactors. A methodology for defining these needs is described. The case of the Molten Chloride Reactor Experiment (MCRE) has been considered and the related investigation highlights the specific needs for reducing uncertainty on the {sup 235}U capture and {sup 35}Cl (n,p) reactions. Relatively inexpensive integral experiments are indicated as possible solutions for significantly reducing the current associated uncertainties. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Measurement of the muon anomalous precession frequency in runs 4, 5, and 6 of the muon ${g}-2$ Experiment at Fermilab

The Fermilab E989 Muon $g-2$ experiment measures the muon's anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon's cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the $g-2$ storage ring. Spin precession frequency is extracted from the time distribution of the muon's decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon's anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world's most precise measurement of the muon's anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗

DOE OSTI2025

Measurement of the muon anomalous precession frequency in runs 4, 5, and 6 of the muon ${g}-2$ Experiment at Fermilab

Israel, Scott Nathan [Boston U.]↗

DOE OSTI2026

Measurement of the muon anomalous precession frequency in Runs 4, 5, and 6 of the Muon g-2 experiment at Fermilab

The Fermilab E989 Muon g − 2 experiment measures the muon’s anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon’s cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the g − 2 storage ring. Spin precession frequency is extracted from the time distribution of the muon’s decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon’s anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world’s most precise measurement of the muon’s anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗

DOE OSTI2022

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition

A critical statistical task in the analysis of shotgun proteomics data involves controlling the false discovery rate (FDR) among the reported set of discoveries. This task is most commonly solved at the peptide-spectrum match (PSM) level by using target-decoy competition (TDC), in which a set of observed spectra are searched against a database containing a mixture of real (target) and decoy peptides. The PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we investigate both PSM-level and peptide-level FDR control methods and come to two conclusions. First, although the TDC procedure is provably correct under certain assumptions, we observe that one of these assumptions - that incorrect PSMs are independent of one another - is frequently violated. Hence, we empirically demonstrate that TDC-based PSM-level FDR estimates can be liberally biased. Further, we propose that researchers avoid PSM-level results and instead focus on peptide-level analysis. Second, we investigate three ways to carry out peptide level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. The most powerful method, peptide-level FDR with PSM competition ("PSM-and-peptide"), carries out competition first at the PSM level and then again at the peptide level. In our experiments, this approach yields an average increase of 17% more discovered peptides at a1% FDR threshold relative to the PSM-only method.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Measuring labor productivity dynamics in U.S. industrial and electric power sectors: a case study (2014–2023)

This study proposes a subsystem methodology for measuring labor productivity in the U.S. industrial and electric power sectors by leveraging public data available between 2014 and 2023. Building on Pasinetti’s framework and subsequent developments, the approach employs Vertically Integrated Sectors (VIS) to account for both direct and indirect productivity effects. The novelty of this work is twofold. First, it enables the estimation of productivity trends over time, providing a robust foundation for empirical analysis. Second, it applies the methodology to a case study of the electric generation sector, highlighting its practical relevance. Using data from the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Impact Analysis for Planning (IMPLAN) tool, the study reveals significant discrepancies between conventional productivity measures and those derived from the VIS approach. Furthermore, the proposed method aligns with the principles of Integrated Energy Systems by capturing the interrelations among generation, distribution, storage, and consumption. This alignment underscores its utility and applications for energy-related policy and planning. Overall, the findings contribute to more precise labor productivity assessments, supporting informed decision-making and future research. Additionally, the method highlights the importance of considering the whole supply chain, providing interrelated metrics for labor productivity which includes both, direct and indirect effects on the final labor productivity metric. By incorporating intersectoral dependencies, this method offers a more comprehensive and accurate measure of labor productivity compared to traditional metrics.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

A Comprehensive Open-Source R Software For Statistical Metrology Calculations: From Uncertainty Evaluation To Risk Analysis

Whether calibrating equipment or inspecting products on the factory floor, metrology requires many complicated statistical calculations to achieve a full understanding and evaluation of measurement uncertainty and quality. In order to assist its workforce in performing these calculations in a consistent and rigorous way, the Primary Standards Lab at Sandia National Laboratories (SNL) has developed a free and open-source software package for computing various metrology calculations from uncertainty propagation to risk analysis. In addition to propagating uncertainty through a measurement model using the well-known Guide to Expression of Uncertainty in Measurement or Monte Carlo approaches, evaluating the individual Type A and Type B uncertainty components that go into the measurement model often requires other statistical methods such as analysis of variance or determining uncertainty in a fitted curve. Once the uncertainty in a measurement has been calculated, it is usually evaluated from a risk perspective to ensure the measurement is suitable for making a particular conformance decision. Finally, SNL’s software can perform all these calculations in a single application via an easy-to-use graphical interface, where the different functions are integrated so the results of one calculation can be used as inputs to another calculation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Experimental Measurements of the Wake of a Sphere at Subcritical Reynolds Numbers

This work experimentally investigated the flow phenomena and vortex structures in the wake of a sphere located in a water loop at Reynolds numbers of Re = 850, 1,250, and 1,700. Velocity fields in the wake region were obtained by applying the time-resolved stereoscopic particle image velocimetry (TR-SPIV) technique. From the acquired TR-SPIV velocity vector fields, the statistical values of mean and fluctuating velocities were computed. Spectral analysis, two-point velocity–velocity cross-correlation, proper orthogonal decomposition (POD) and vortex identification analyses were also performed. The velocity fields show a recirculation region that decreases in length with an increase of Reynolds numbers. The power spectra from the spectral analysis had peaks corresponding to a Strouhal number of St = 0.2, which is a value commonly found in the literature studies of flow over a sphere. The two-point cross-correlation analysis revealed elliptical structures in the wake, with estimated integral length scales ranging between 12% and 63% of the sphere diameter. The POD analysis revealed the statistically dominant flow structures that captured the most flow kinetic energy. It is seen that the flow kinetic energy captured in the smaller scale flow structures increased as Reynolds number increased. The POD modes contained smaller structure as the Reynolds number increased and as mode order increased. In addition, spectral analysis performed on the POD temporal coefficients revealed peaks corresponding to St = 0.2, similar to the spectral analysis on the fluctuating velocity. The ability of POD to produce low-order reconstructions of the flow was also utilized to facilitate vortex identification analysis, which identified average vortex sizes of 0.41D for Re1, 0.33D for Re2, and 0.32D for Re3.

Engineering↗

DOE OSTI2022

Modern analogs for understanding pollen-vegetation dynamics in a Mediterranean mosaic landscape (Balearic Islands, Western Mediterranean)

This paper presents the study of modern pollen analogs from the Balearic Islands. While similar studies have been largely applied to mainland areas, research focused on modern vegetation dynamics on Mediterranean islands remains very rare. In this research, we combine vegetation surveys, pollen analysis and multivariate statistics to understand landscape composition. The main objectives of are: (1) to examine pollen-vegetation relationships in relation to environmental and land-use variables; (2) to understand modern pollen representation in a mosaic landscape structure; and (3) to propose pollen indicators that characterize the primary vegetation types from the Balearic Islands to better interpret past pollen records in Mediterranean island environments. Pollen results and Redundancy Analysis (RDA) distinguish three major groups: (a) Holm oak and box formations; (b) maquis and garrigues; and (c) anthropogenic and open habitats. Landscape form, mean decadal rainfall, mean decadal temperature, fire activity, trampling, slope percentage, wet/flooded soil, saline soil, distance to agropastoral cells, gHM index, domestic herbivory presence, agropastoral use, and soil type are the major variables explaining modern pollen assemblage variation in our research. Poaceae undiff., Plantago sp., Apiaceae undiff., Cerealia-t, and Cichorieae are highly correlated to human activities but should be interpreted cautiously when occurring in low values. Quercus ilex-t, Hypericum, and Buxus are correlated to humid locations while Pistacia, Pinus, Juniperus-t, and Olea to high mean decadal temperatures. Our study indicates how pollen analysis and multivariate analysis are powerful tools for characterizing the mosaic landscape, with special focus on the main vegetation types of the Balearic Islands.

Geology↗

DOE OSTI2019

Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis

The ability to discover minute differences between samples or sample classes for gas chromatography coupled to mass spectrometry (GC-MS) can be a challenging endeavor, especially when those differences are not a priori . Fisher ratio (F-ratio) analysis is an apt technique to probe the differences between GC-MS chromatograms. F-ratio analysis is a supervised, non-targeted, discovery-based method that compares two different samples (or sample classes) to reduce the GC-MS dataset into a hit list composed of class distinguishing compounds. Three different F-ratio techniques, peak table, tile, and pixel-based were used to “discover” nine non-native analytes that were spiked into gasoline at four different nominal concentrations of 250, 85, 25, 5 parts-per-million (ppm). For the tile and pixel-based F-ratio calculations, a novel methodology is introduced to improve the sensitivity of the F-ratio calculations while reducing false positives. Furthermore, we use a combinatorial technique using null class comparisons, termed null distribution analysis, to determine a statistical F-ratio cutoff for analysis of the hit lists. The pixel-based algorithm was the most sensitive method and was able to “discover” all nine spiked analytes at a nominal concentration of 250 ppm albeit with one false positive interspersed towards the bottom of the hit list. The pixel-based software was also able to “discover” more of the spiked analytes at the lower concentrations with seven of the spiked analytes “discovered” at 85 ppm, four of the spiked analytes “discovered” at 25 ppm, and one analyte “discovered” at 5 ppm.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

A road map to cosmological parameter analysis with third-order shear statistics: III. Efficient estimation of third-order shear correlation functions and an application to the KiDS-1000 data

Context. Third-order lensing statistics contain a wealth of cosmological information that is not captured by second-order statistics. However, the computational effort it takes to estimate such statistics in forthcoming stage IV surveys is prohibitively expensive. Aims. We derive and validate an efficient estimation procedure for the three-point correlation function (3PCF) of polar fields such as weak lensing shear. We then use our approach to measure the shear 3PCF and the third-order aperture mass statistics on the KiDS-1000 survey. Methods We constructed an efficient estimator for third-order shear statistics that builds on the multipole decomposition of the 3PCF. We then validated our estimator on mock ellipticity catalogs obtained from N -body simulations. Finally, we applied our estimator to the KiDS-1000 data and presented a measurement of the third-order aperture statistics in a tomographic setup. Results. Our estimator provides a speedup of a factor of ∼100–1000 compared to the state-of-the-art estimation procedures. It is also able to provide accurate measurements for squeezed and folded triangle configurations without additional computational effort. We report a significant detection of tomographic third-order aperture mass statistics in the KiDS-1000 data (S/N = 6.69). Conclusions. Our estimator will make it computationally feasible to measure third-order shear statistics in forthcoming stage IV surveys. Furthermore, it can be used to construct empirical covariance matrices for such statistics.

Astronomy & Astrophysics↗

DOE OSTI2021

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

79 ASTRONOMY AND ASTROPHYSICS↗