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DOE OSTI2023

On Integral Benchmarks for Resolving TSL Conflicts for ENDF/B-VIII.1 Release [Slides]

This presentation touches on polystyrene ORNL evaluation in TSL review process for ENDF/B-VIII.1. the presentation found excellent agreement with INS and transmission measurements. Additionally, PS and PE evaluations with different phonon spectra, similar total xs., calculate keff similarly. This presentation found for critical benchmarks. While extremely useful for validation of nuclear data at all energies, critical benchmarks are not the best tool to provide a definitive answer on conflicting TSLs. In conclusion this presentation found INS and transmission measurements need to be the basis of validation of TSLs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗

DOE OSTI2023

Vectorization of Dynamic Subgraphs via Generative Models (Final Report)

An important class of data analysis tasks stem from comparing subsets of connected records within massive sets of complex relational data. A common approach is to represent each set of connected records with a small graph, or set of data entities (graph vertices) and their relationships (graph edges), and efficient methods to gauge similarity for pairs of graphs are of high interest. This project concentrated on dynamic graphs, where each edge record has an associated timestamp denoting the time of observation. Pre-existing techniques for comparing dynamic graphs concentrate on either computing graph edit distance (number of vertex and edge deletion, addition, and timestamp modifications) or vectorizing the graph with counts of a limited set of dynamic graph motifs (tiny fundamental subgraphs) and computing distances between the vectors. These approaches are less able to see similarities in graphs that are fairly different in size but come from identical graph generation processes. The motif counting approach can be improved for graphs from the same process, but suffers from requiring many types of motifs meaning it is expensive. Moreover, many motifs are not present for small graphs, meaning realizing a a much larger graph came from the same process is difficult.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Nramp: Deprive and conquer?

Solute carriers 11 (Slc11) evolved from bacterial permease (MntH) to eukaryotic antibacterial defense (Nramp) while continuously mediating proton (H+)-dependent manganese (Mn2+) import. Also, Nramp horizontal gene transfer (HGT) toward bacteria led to mntH polyphyly. Prior demonstration that evolutionary rate-shifts distinguishing Slc11 from outgroup carriers dictate catalytic specificity suggested that resolving Slc11 family tree may provide a function-aware phylogenetic framework. Hence, MntH C (MC) subgroups resulted from HGTs of prototype Nramp (pNs) parologs while archetype Nramp (aNs) correlated with phagocytosis. PHI-Blast based taxonomic profiling confirmed MntH B phylogroup is confined to anaerobic bacteria vs. MntH A (MA)’s broad distribution; suggested niche-related spread of MC subgroups; established that MA-variant MH, which carries ‘eukaryotic signature’ marks, predominates in archaea. Slc11 phylogeny shows MH is sister to Nramp. Site-specific analysis of Slc11 charge network known to interact with the protonmotive force demonstrates sequential rate-shifts that recapitulate Slc11 evolution. 3D mapping of similarly coevolved sites across Slc11 hydrophobic core revealed successive targeting of discrete areas. The data imply that pN HGT could advantage recipient bacteria for H+-dependent Mn2+ acquisition and Alphafold 3D models suggest conformational divergence among MC subgroups. It is proposed that Slc11 originated as a bacterial stress resistance function allowing Mn2+-dependent persistence in conditions adverse for growth, and that archaeal MH could contribute to eukaryogenesis as a Mn2+ sequestering defense perhaps favoring intracellular growth-competent bacteria.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Polarimetric Geometric Modeling for mm-VLBI Observations of Black Holes

The Event Horizon Telescope (EHT) is a millimeter very long baseline interferometry (VLBI) array that has imaged the apparent shadows of the supermassive black holes M87* and Sagittarius A*. Polarimetric data from these observations contain a wealth of information on the black hole and accretion flow properties. In this work, we develop polarimetric geometric modeling methods for mm-VLBI data, focusing on approaches that fit data products with differing degrees of invariance to broad classes of calibration errors. We establish a fitting procedure using a polarimetric “m-ring” model to approximate the image structure near a black hole. By fitting this model to synthetic EHT data from general relativistic magnetohydrodynamic models, we show that the linear and circular polarization structure can be successfully approximated with relatively few model parameters. We then fit this model to EHT observations of M87* taken in 2017. In total intensity and linear polarization, the m-ring fits are consistent with previous results from imaging methods. In circular polarization, the m-ring fits indicate the presence of event-horizon-scale circular polarization structure, with a persistent dipolar asymmetry and orientation across several days. The same structure was recovered independently of observing band, used data products, and model assumptions. Despite this broad agreement, imaging methods do not produce similarly consistent results. Our circular polarization results, which imposed additional assumptions on the source structure, should thus be interpreted with some caution. Polarimetric geometric modeling provides a useful and powerful method to constrain the properties of horizon-scale polarized emission, particularly for sparse arrays like the EHT.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Smart Planning Support for Nuclear Power Plant Work Planners

Operation and Maintenance costs of a nuclear power plant account for 60% of overall plant costs due to the nuclear industry?s risk-adverse culture (World Nuclear Association, 2020). The industry relies on highly conservative estimates to avoid risk and brute force reactions to manage the unexpected and keep a plant running safely. This approach has kept plants operating safer than any other power source available over the last six decades (Ritchie, 2020). However, these conservative methods have cost the nuclear industry in market competitiveness. The solution? Transform the conservative approaches to accurate, efficient, and still safe data-driven approaches that leverage advanced technologies available in the digital age of operation. This project endeavored to identify two key aspects to successfully incorporate historical work execution data. First, where in the work management process is that information most pertinent? Next, what variables are key to informing the best strategy for organizing the upcoming work? The Smart Planner uses data collected by dynamic work instructions to provide a basis for suggesting requirements of similar, upcoming work packages. Further, it curbs inefficiencies of the work management process by analyzing the likelihood of discovery work, offering suggestions to build a contingency package. All this taking place as the work planner builds work packages that meet the predetermined scope of work to be performed.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗

DOE OSTI2024

Radial Deconsolidation Leach-Burn-Leach of Five AGR-3/4 TRISO Fuel Compacts after Post-Irradiation Heating Tests

Five AGR-3/4 fuel compacts were previously heated in the Fuel Accident Condition Simulator (FACS) furnace. Three of these compacts were also reirradiated in the Neutron Radiography (NRAD) reactor before the FACS tests. One of these was heated to 1200°C, two were heated to 1400°C, one was heated to 1600°C, and one was held for a period of time at 1600 and 1700°C. After these FACS tests, the compacts were subjected to destructive post-irradiation examination (PIE) via radial-deconsolidation-leach-burn-leach (RDLBL) at INL. The RDLBL process deconsolidated the compacts in multiple radial steps, followed by a final, single-step axial deconsolidation. The samples generated at each step were analyzed for isotopes of key fission products and actinides. After each deconsolidation step, the compact volume was assessed, and this was used to normalize the measured quantities of nuclides of interest to give volumetric concentrations as a function of radial position within the compact. The total inventories of measured fission products and actinides and the radial concentration profiles were compared with their sibling compacts within the same capsule (similar irradiation conditions) that went through RDLBL in the as-irradiated state. These data will be used to refine the fission product transport models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Testing Scale-Dependent Modified Gravity with DESI DR1

The Dark Energy Spectroscopic Instrument (DESI) provides an unprecedented opportunity to test deviations from general relativity (GR) that introduce a new physical scale within its redshift range. Using the connection between a Yukawa-like potential and the Hu-Sawicki $f(R)$ model, we place strong constraints on the range of a hypothetical fifth force mediated by a massive scalar field. We analyze the power spectrum measurements from DESI Data Release 1 using a baseline EFT model that employs the fkpt approach for the loop integrals. We find no evidence for deviations from GR and obtain the constraint $\log_{10} |f_{R_0}| < -4.59$ (95% C.L.). This corresponds to an upper bound at redshift zero on the scale at which corrections to GR become important, $λ< 17.81$ Mpc, or equivalently, a lower bound on the mass of the additional gravitational mediator of $m_ϕ> 3.60 \times 10^{-31}$ eV. We find that the modified gravity parameter $f_{R_0}$ is largely orthogonal to the cosmological parameters in the model, such that no additional projection effects relative to the GR case are introduced in this Full-Shape analysis. Furthermore, a second modified gravity parameter, the power index $n$, which modulates the time-variation of the associated mass, is found to be consistent with previous analyses that fixed it to unity. Adding DESI BAO data or other cosmological probes does not significantly change these results. The conclusions remain similar if the background evolution is described by evolving dark energy instead of a cosmological constant. Additionally, we test the robustness of the baseline model by varying the maximum wavenumber used in the Full-Shape analysis and analyzing the DESI targets separately. Finally, we analyze the degeneracies between the modified-gravity parameters and the sum of neutrino masses.

Gonzalez, D. [Guanajuato U.; UNAM, CCF] (ORCID:000↗

DOE OSTI2021

Confirmation of PNNL Quantitative Infrared Cross-Sections for Isobutane

The Pacific Northwest National Laboratory (PNNL) gas-phase database is a compilation of quantitative experimental (5, 25, 50 °C) infrared spectra of ca. 500 molecules, designed for in situ, standoff or remote sensing of gases and vapors at or near atmospheric pressure. The data are characterized by calibration on both the wavenumber and intensity axes. Recent papers have called into question the PNNL intensity values for isobutane, (2-methylpropane, HC(CH 3 ) 3 ) suggesting discrepancies of 30 to 40%. In this study we re-measure and re-examine the intensity values of isobutane using both similar and alternate methods to those used to generate the original PNNL database spectra. Here, indirect confirmation from literature data of homologous molecules and direct confirmation from new results confirm that for many band integrals across the isobutane spectrum, the original PNNL data are indeed accurate to within the reported 3% experimental uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Effect of Nuclear Data Covariances on Integral Experiment Design with Sensitivity and Uncertainty Analysis

Washington River Protection Solutions (WRPS) uses MCNP6.2 and the Whisper code for criticality safety analyses of the Hanford Tank Farm. Together the codes derive baseline upper subcritical limits (USLs) for the waste models using experimental benchmarks. Whisper returns higher USLs, i.e. , has less of a conservative penalty, when the neutronic similarity of the experimental benchmarks to the application is high. Unfortunately, few critical benchmarks have high similarity to the Hanford tanks. The waste in the tanks is highly dilute in plutonium and contains large masses of weakly neutron-absorbing elements like iron and manganese. Experimental benchmarks typically have low sensitivity to these absorbers because they are present as structural materials. Lacking similar benchmarks, new Thermal Epithermal eXperiment (TEX) configurations with high Pu content and interstitial iron absorbers have been designed for the criticality safety validation. The features of the design have been iterated upon to maximize the similarity between the experiment and different Hanford waste models. The similarity is quantified with sensitivity analysis and uncertainty quantification using the representativity coefficient, or c k . The representativity calculation requires nuclear data covariances, which may differ between nuclear data libraries and between library versions. Because of these variations, the optimal design may depend on the nuclear data covariances library. A scenario can be envisioned where an experiment is designed, and c k is maximized, with one set of covariance data. However, when the covariance data is changed, say from ENDF/B-VII.1 to ENDF/B-VIII.0, and the benchmark is used in a criticality safety evaluation, the experiment becomes suboptimal with respect to c k . In this paper, we present how the optimal design of the new TEX experiments varied depending on the nuclear data covariances used to calculate c k . We compare ENDF/B-VII.1 and ENDF/B-VIII.0, as if the library had been updated since the design of the experiment. Additionally, we use JEFF3.3 to simulate if the covariance data of a different library had been used. The results show that the covariances do have an important effect on the designs, less so for thermal systems (where the data are more consistent between evaluations) and more so for epithermal systems where more differences exist.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

AGC 3 Irradiated Material Properties Analysis

This report documents the analysis of the irradiated material property data from the Advanced Graphite Creep (AGC)-3 graphite specimens. This is the third in a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades. The AGC-3 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 800°C, beginning with irradiation Cycle 152B on November 28, 2012 and ending with Cycle 155B on April 12, 2014, with a total received dose range of 0.9–3.7 dpa. Larger creep and control specimens located more centrally in the capsule received a dose of 1.0 – 3.7 dpa. AGC-3 was designed to provide irradiation conditions similar to AGC-1 and AGC-2 capsules (similar graphite grades tested, specimen dimensions, mechanical loading conditions) but at a different nominal irradiation temperature of 800°C. AGC-3 was irradiated for a short duration to provide material property values at lower dose levels. AGC-4 will have a longer duration and provide material property values at higher dose levels. After irradiation, material property and dimensional strain measurements were conducted on all AGC-3 specimens (from 11 nuclear graphite grades) using the same equipment and approved standards as were conducted before irradiation. The specimen loading configuration for all graphite grades within AGC-3 followed a similar pattern as earlier AGC capsules to provide easy future comparison of all irradiated material property data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Application of the Rossi-alpha method to simulations of HEU and organic scintillators

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook contains thousands of high-quality benchmark measurements that include detailed descriptions of experiment designs and exhaustive accounting of uncertainties. These benchmarks are used extensively by the criticality safety and nuclear data communities. A subcritical benchmark involving bare, highly enriched uranium (HEU) has yet to be accepted to the handbook. The Measurement of Uranium Subcritical and Critical (MUSiC) was proposed to address this gap. MUSiC will be performed at the National Criticality Experiments Research Center (NCERC) and will leverage bare HEU hemisphere shells that can be assembled in a variety of configurations. The measurement has the following key benefits: it will be the first set of fast, subcritical measurements of HEU to be submitted to ICSBEP; ten configurations will be measured spanning from nearly no multiplication to critical (e.g., it is a measurement of similar systems with different re activities); measurements of an all-HEU system will be of value for nuclear data validation, providing results that assist in determining biases in both measurements and simulations. A variety of detector systems will be used including a Neutron Multiplicity Array Detector (NoMAD) system (similar to the MC-15), four small volume 0.635 cm (#8960;) × 7.59 cm 3He detectors (ideal for measuring prompt neutron decay constants due to their fast recovery speed), and an array of eight 5.08 cm (#8960;) × 5.08 cm EJ-309 organic scintillator detectors. The focus of this work is the simulation of the subcritical MUSiC configurations including the EJ-309 detectors and the application of the Rossi-α method. The EJ-309 organic scintillator detector array is a new detection system that will be deployed at NCERC. Organic scintillators are ideal for measurements of fast, subcritical systems due to their short time response on the order of nanoseconds.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

An Assessment of Non-Powered Dam Hydropower Development Opportunities in the United States

Retrofitting non-powered dams (NPDs) to add hydropower offers multiple benefits over traditional, new hydropower development. Since much of the civil works infrastructure already exists, many retrofits require minimal new construction and can leverage existing water conveyances and discharge for generation. Although NPDs vary considerably in terms of their defining characteristics, identifying similarities helps support targeted investment and find opportunities to develop solutions for common challenges. With the publication of NPD-related data in 2022 (Hansen et al. 2022b), the US Department of Energy and relevant stakeholders are equipped with key information across roughly 89,000 US NPDs. These data represent the best-available information to date and extend beyond previous assessments of potential power capacity by describing design, operational, socioeconomic, environmental aspects of NPDs (Hadjerioua et al. 2012). To capitalize on these efforts to improve breadth and depth of NPD data access, this study uses a data-driven approach to assess NPD hydropower development opportunities in the United States. To help describe project feasibility drivers for NPDs, recent NPD retrofits were reviewed to examine variability with respect to a variety of characteristics. Based on the assessment of recent retrofits and data availability, several attributes were selected to characterize the remaining NPD population: (1) owner type, (2) 30% exceedance flow (a common design flow that describes the flow that is exceeded by 30% of the flow in the record), (3) hydraulic head, and (4) maximum reservoir storage. These four characteristics were used as inputs to a statistical clustering analysis (a common technique for grouping individuals of a population based on similarities or how closely associated individuals are to one another) of a set of 2,709 NPDs with at least 100 kW potential capacity and recently retrofit dams with available data. With the large dataset of NPDs, the clusters help describe how the population breaks down into different types (i.e., how many types of dams there are and what portion of the population belongs to each cluster).

13 HYDRO ENERGY↗

DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar

This report concerns Root water content (RWC) as a vital component in water flux in soil-plant-atmosphere continuum. Knowledge of RWC helps to better understand the root function and the soil-root interaction and improves water cycle modeling. However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study, we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively. An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66% to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating RWC with similar accuracy, i.e., the prediction error [root-mean-square error (RMSE)] was less than 8% for the simulation data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data. This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root study and the understanding and modeling of ecohydrology in the rhizosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Charged particle reconstruction in CLAS12 using Machine Learning

In this work, we present studies of track parameter reconstruction from raw information in CLAS12 detector's Drift Chambers, using Machine Learning (ML). We study the resolution of tracks reconstructed with different types of ML models/algorithms, including Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Gradient Boosting Trees (GBT) using simulated data. We find that the resulting ML model is capable of reconstructing track parameters (particle momentum, and polar and azimuthal angles) with accuracy similar to Hit Based (HB) tracking code, but $150$ times faster. Moreover, physics reactions can be identified using the particles reconstructed by the neural network in real-time (with a rate of about $34~kHz$) during experimental data collection. The developed model can be used in numerous applications, such as triggering specific physics reactions in real-time, detector performance monitoring, and real-time detector calibration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗