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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Unraveling the Effects of Melt–Mantle Interactions on the Gold Fertility of Magmas

The oxidation state of the Earth’s mantle and its partial melting products exert a key control on the behavior and distribution of sulfur and chalcophile and siderophile elements between the mantle and crust, underpinning models of ore deposit formation. Whether the oxidized nature of magmas is inherited from the asthenospheric mantle source or acquired during ascent and differentiation is vigorously debated, limiting our understanding of the mechanisms of extraction of sulfur and metals from the mantle. Here, we focused on the redox-sensitive behavior of sulfur in apatite crystallized from quenched alkaline basaltic melts preserved within a peridotite xenolith from the El Deseado Massif auriferous province in southern Patagonia. We took advantage of this unique setting to elucidate the redox evolution of melts during their ascent through the subcontinental lithospheric mantle (SCLM) and grasp the inner workings of the Earth’s mantle during gold metallogenesis. Our data reveal that an initially reduced silicate melt (ΔFMQ -2.2 to -1.2) was oxidized to ΔFMQ between 0 and 1.2 during percolation and interaction with the surrounding peridotite wall-rock (ΔFMQ 0 to +0.8). This process triggered changes in sulfur speciation and solubility in the silicate melt, boosting the potential of the melt to scavenge ore metals such as gold. We suggest that large redox gradients resulting from the interaction between ascending melts and the surrounding mantle can potentially modify the oxidation state of primitive melts and enhance their metallogenic fertility. Among other factors including an enriched metal source and favorable geodynamic conditions, redox gradients in the mantle may exert a first-order control on the global-scale localization of crustal provinces endowed with gold deposits.

58 GEOSCIENCES↗
DOE OSTI2020

Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Timelike Compton Scattering from a longitudinally polarised target with CLAS12 at Jefferson Lab

Explorations into the internal dynamics of hadrons are constantly evolving, and the requirement for experimental results to verify theoretical models of hadron structure is paramount. A key area in this field is the study of Generalised Parton Distributions (GPDs), which are functions used to model the momenta of quarks and gluons within hadrons, and the methods to access GPDs experimentally. One such scattering process that allows access to these is Timelike Compton Scattering. TCS complements existing Deeply Virtual Compton Scattering experiments and allows investigation into the universality of GPDs through access to the real and imaginary parts of the parton helicity independent GPD Hq via beam spin asymmetries (BSA), and it provides novel access to the real and imaginary parts of the parton helicity dependent GPD ˜Hq through target polarisation asymmetries (TSA). This thesis work presents a comparative study with the first published BSA for TCS at the Thomas Jefferson National Accelerator Facility (JLab), alongside a first time extraction of a Target Spin Asymmetry with the Summer 2022 data taking run. JLab hosts the Continuous Electron Beam Accelerator Facility (CEBAF) which provides a 12 GeV electron beam to four experimental halls. Hall-B contains the CEBAF Large Acceptance Spectrometer, which took data across three run periods on a longitudinally polarised NH3 and ND3 fixed target from 2022-2023, to extract measurements of electron-proton scattering, from which a TCS signal could be extracted. The thesis discusses work done to understand and eliminate contributions from the non-/low-polarised nuclear background, testing pre-established cuts to eliminate pion background from a dilepton (e+e-) final state and modifying them as needed for the new experimental run, and attempts to hone in on a clean TCS signal from which to extract the two asymmetry observables. A comparison with existing BSA results was performed; however, the statistical errors are too large to draw a significant conclusion as to whether there is agreement across each bin. More data is needed for a multidimensionally binned extraction. A proof of principle was achieved in the TSA measurements, with two out of four kinematic bins showing preliminary agreement in shape with theoretical values. Again, the errors are significant due to the contributions from the nuclear background. To support these conclusions, a further study was done, which takes into account an estimate of the asymmetries with the full available dataset (this thesis is based only on data taken in the summer set; at the time of writing processing was still being conducted for the final two datasets), as well as an estimate including additional future experiment days that were awarded in July 2024. Additional work was done on a secondary project exploring the feasibility of measuring TCS at the upcoming Electron Ion Collider, supporting the design proposal for the detector for the first interaction region and giving a positive outlook for the future of these types of measurements beyond JLab.

Gates, Kayleigh [Univ. of Glasgow, Scotland (Unite↗
DOE OSTI2026

Irrigation Infrastructure and Modernization – Setting a Baseline: Estimates of Irrigation Water Conveyance Infrastructure Extents and Composition and the Potential Water, Energy, and Economic Benefits of Modernization in the Western U.S.

In the Western United States (U.S.), water delivery for irrigation is still largely managed using century-old equipment and designs. Modernization of this vital water conveyance infrastructure, such as piping of earthen canals, is known to improve water availability and water quality for farmers, while saving energy and enabling new hydropower. However, there is sparse information about the extent of irrigation water delivery infrastructure, which makes it challenging to estimate the cost of upgrades at scale and the potential benefits of accelerating modernization work. This report estimates a variety of previously unquantified data points related to irrigation water delivery infrastructure in the Western U.S. to support stakeholders interested in nationwide modernization planning. The findings should be considered approximations, useful for understanding the scale, range, or variability of these indicators. Taken together, the findings of this report illustrate some of the challenges and opportunities involved in modernizing the agricultural water delivery infrastructure in the Western U.S. Accelerating the pace of modernization could strengthen the long-term resilience of U.S. food systems while providing significant economic, energy, water, and environmental benefits.

13 HYDRO ENERGY↗
DOE OSTI2023

ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

From +I to +IV, Alkalis to Actinides: Capturing Cations across the Periodic Table with Keggin Polyoxometalate Ligands

Coordination chemistry trends across the periodic table are often difficult to probe experimentally due to limitations in finding a versatile but consistent chelating platform that can accommodate various elements without changing its coordination mode. Herein, we present new metal/ligand systems covering a wide range of ionic radii, charges, and elements. Five different ligands derived from the Keggin structure (HBW 11 O 39 8– , PW 11 O 39 7– , SiW 11 O 39 8– , GeW 11 O 39 8– , and GaW 11 O 39 9– ) were successfully crystallized with six different cations (Na + , Sr 2+ , Ba 2+ , La 3+ , Ce 4+ , and Th 4+ ) and characterized by single-crystal X-ray diffraction. Twenty-five new compounds were obtained by using Cs + as the counterion, yielding a consistent base formula of Cs x [M(XW 11 O 39 ) 2 ]·nH 2 O. Despite having a similar first-coordination sphere geometry (i.e., 8-coordinated), the nature of the central cation was found to impact the long-range geometry of the complexes. This unique crystallographic data set shows that, despite the traditional consensus, the local geometry of the cation (i.e., metal–oxygen bond distance) is not enough to depict the full impact of the complexed metal ion. The bending and twisting of the complexes, as well as ligand–ligand distances, were all impacted by the nature of the central cation. We also observed that counterions play a critical role by stabilizing the geometry of the M(XW 11 ) 2 complex and directing complex–complex interactions in the lattice. We also define certain structural limits for this type of complex, with the large Ba 2+ ion seemingly approaching those limits. Finally, this study thus lays the foundation for capturing the coordination chemistry of other rarer elements across the periodic table such as Ra 2+ , Ac 3+ , Bk 4+ , Cf 3+ , etc.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Analyzing Prospects for Quantum Advantage in Topological Data Analysis

Lloyd [Nat. Commun. , 10138 (2016)] were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that superquadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for superpolynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where superpolynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
DOE OSTI2025

Measured Reaction Rate Ratios of 235 U, 238 U, 237 Np, 239 Pu Samples in the PFUNS 235 U Prompt Fission Neutron Spectrum Criticality Experiment

The Prompt Fission Uranium Neutron Spectrum experiment, an experiment to reduce uncertainties in the high energy tail of the 235 U prompt fission spectrum, achieved success by performing two separate irradiations measuring approximately 40 different IRDFF-II reactions total using over 20 different foil materials at the National Criticality Experiments Research Center in February 2024. The criticality experiment utilized a set of highly enriched uranium hemispherical shells of increasing diameters with a large void in the center where the samples were located. The focus of this work is the first of two PFUNS irradiations focused on irradiating two of each fission foils, one bare and one cadmium covered, along with metal activation foils containing reaction products with short half-lives, such as indium, iron, and gold along with nickel fluence monitors. This work focuses on presenting the initial reaction rate ratio results of the fission foils from the aforementioned first irradiation to assist in nuclear data validation of those species in a nearly pure 235 U prompt fission neutron spectrum and compares to the Lady Godiva and Flattop-25 historic experiments at the Los Alamos Critical Experiments Facility. Future work will combine fission foil and metallic activation foil reaction rate ratio results from both the first and second higher power irradiation and reaction rates for a final spectral adjustment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Operational-based annual energy production uncertainty: are its components actually uncorrelated?

Calculations of annual energy production (AEP) from a wind power plant – whether based on preconstruction or operational data – are critical for wind plant financial transactions. The uncertainty in the AEP calculation is especially important in quantifying risk and is a key factor in determining financing terms. A popular industry practice is to assume that different uncertainty components within an AEP calculation are uncorrelated and can therefore be combined as the sum of their squares. We assess the practical validity of this assumption for operational-based uncertainty by performing operational AEP estimates for more than 470 wind plants in the United States, mostly in simple terrain. We apply a Monte Carlo approach to quantify uncertainty in five categories: revenue meter data, wind speed data, regression relationship between density-corrected wind speed (from reanalysis data) and measured wind power, length of long-term-correction data set, and future interannual variability. We identify correlations between categories by comparing the results across all 470 wind plants. We observe a positive correlation between interannual variability and the linearized long-term correction; a negative correlation between wind resource interannual variability and linear regression; and a positive correlation between reference wind speed uncertainty and linear regression. Then, we contrast total operational AEP uncertainty values calculated by omitting and considering correlations between the uncertainty components. We quantify that ignoring these correlations leads to an underestimation of total AEP uncertainty of, on average, 0.1% and as large as 0.5% for specific sites. Although these are not large increases, these would still impact wind plant financing rates; further, we expect these values to increase for wind plants in complex terrain. Based on these results, we conclude that correlations between the identified uncertainty components should be considered when computing the total AEP uncertainty.

17 WIND ENERGY↗
DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of composition and symmetry energy on the temperature of quasiprojectiles simulated with antisymmetrized molecular dynamics

The equation of state describes the emergent physical properties of matter. Experimental data is needed to help constrain the equation of state for nuclear matter. These constraints can help distinguish between an “asy-stiff” and an “asy-soft” equation of state, which has astrophysical implications. One path to help constrain the models is to analyze the nuclear caloric curve; some experiments have shown dependence on neutron excess, and may thus be sensitive to the asymmetry. A difference in the caloric curve based on the asymmetry of the reconstructed quasiprojectile (QP) had been observed using 70 Zn on 70 Zn at 35 MeV/nucleon taken with the NIMROD array. Antisymmetrized molecular dynamics calculations were performed for the same system and deexcited with gemini++. Both Gogny (asy-soft) and Gogny-as (asy-stiff) data sets were generated. The particles were then filtered based on detector geometric acceptance and thresholds. From the accepted particles, the excitation energy and temperature were calculated in the same way as for experimental data. Additionally, filter effects on the observed nuclear caloric curves were investigated. A tendency for the asy-stiff nuclear caloric curves to have higher temperatures than their asy-soft counterparts was observed for a number of probes. In addition, some probes may show sensitivity to the reconstructed composition of the QP, but this is inconclusive due to high statistical fluctuations and a large dependence on the exact method of event selection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Automatic Image Point Matching

Sparse Image Point Matching (SIPM) is a foundational technology for photo triangulation, structure from motion (SfM), Simultaneous Location and Mapping (SLAM), and data fusion. The goal of the matching is to automatically generate sets of image coordinates that identify the same feature across images. Ideally, the process should be robust to lighting, scale, perspective, and modality changes. The scope of the image matching topic in the field of remote sensing (RS) is enormous because of the variety of collection platforms, modalities, sensor types, applications, and subjects. In this work, we report the history of and assess the state of the art of visible-spectrum (panchromatic and color) image matching of the Earth’s surface. Work specific to large-format images (LFI) (e.g., metric aerial cameras and Earth-observing satellites) will be highlighted. However, the state of the art in this century will mostly be traced through machine vision research and benchmarks because research specific to LFI is rare.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Biofuels Induced Land Use Change Emissions: The Role of Implemented Land Use Emission Factors

Biofuels’ induced land-use change (ILUC) emissions have been widely studied over the past 15 years. Many studies have addressed uncertainties associated with these estimates. These studies have broadly examined uncertainties associated with the choice of economic models, their assumptions and parameters, and a few bio-physical variables. However, uncertainties in land-use emission factors that represent the soil and vegetation carbon contents of various land types across the world and are used to estimate carbon fluxes due to land conversions are mostly overlooked. This paper calls attention to this important omission. It highlights some important sources of uncertainty in land-use emissions factors, explores the range in these factors from established data sources, and compares the influence of their variability on ILUC emissions for several sustainable aviation fuel (SAF) pathways. The estimated land-use changes for each pathway are taken from a well-known computable general equilibrium model, GTAP-BIO. Two well-known carbon calculator models (CCLUB and AEZ-EF) that represent two different sets of emissions factors are used to convert the GTAP-BIO estimated land-use changes to ILUC emissions. The results show that the calculated ILUC emissions obtained from these carbon calculators for each examined SAF pathway are largely different, even for the same amortization time horizon. For example, the ILUC emissions values obtained from the AEZ-EF and CCLUB models for producing jet fuel from corn ethanol for a 25-year amortization period are 24.9 gCO 2 e/MJ and 15.96 gCO 2 e/MJ, respectively. This represents a 60% difference between the results of these two carbon calculators for the same set of land-use changes. The results show larger differences for other pathways as well.

09 BIOMASS FUELS↗