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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗
DOE OSTI2021

Current Status of Radiopharmaceutical Therapy

In radiopharmaceutical therapy (RPT), a radionuclide is systemically or locally delivered with the goal of targeting and delivering radiation to cancer cells while minimizing radiation exposure to untargeted cells. Examples of current RPTs include thyroid ablation with the administration of {sup 131}I, treatment of liver cancer with {sup 90}Y microspheres, the treatment of bony metastases with {sup 223}Ra, and the treatment of neuroendocrine tumors with {sup 177}Lu-DOTATATE. New RPTs are being developed where radionuclides are incorporated into systemic targeted therapies. To assure that RPT is appropriately implemented, advances in targeting need to be matched with advances in quantitative imaging and dosimetry methods. Currently, radiopharmaceutical therapy is administered by intravenous or locoregional injection, and the treatment planning has typically been implemented like chemotherapy, where the activity administered is either fixed or based on a patient’s body weight or body surface area. RPT pharmacokinetics are measurable by quantitative imaging and are known to vary across patients, both in tumors and normal tissues. Therefore, fixed or weight-based activity prescriptions are not currently optimized to deliver a cytotoxic dose to targets while remaining within the tolerance dose of organs at risk. Methods that provide dose estimates to individual patients rather than to reference geometries are needed to assess and adjust the injected RPT dose. Accurate doses to targets and organs at risk will benefit the individual patients and decrease uncertainties in clinical trials. Imaging can be used to measure activity distribution in vivo, and this information can be used to determine patient-specific treatment plans where the dose to the targets and organs at risk can be calculated. The development and adoption of imaging-based dosimetry methods is particularly beneficial in early clinical trials. In this work we discuss dosimetric accuracy needs in modern radiation oncology, uncertainties in the dosimetry in RPT, and best approaches for imaging and dosimetry of internal radionuclide therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Computational Modeling of Heterogeneity of Stress, Charge, and Cyclic Damage in Composite Electrodes of Li-Ion Batteries

Charge heterogeneity is a prevalent feature in many electrochemical systems. In a commercial cathode of Li-ion batteries, the composite is hierarchically structured across multiple length scales including the sub-micron single-crystal primary-particle domains up to the macroscopic particle ensembles. The redox kinetics of charge transfer and mass transport strongly couples with mechanical stresses. This interplay catalyzes substantial heterogeneity in the charge (re)distribution, stresses, and mechanical damage in the composite electrode during charging and discharging. We assess the heterogeneous electrochemistry and mechanics in a LiNi x Mn y Co z O 2 (NMC) cathode using a fully coupled electro-chemo-mechanics model at the cell level. A microstructure-resolved model is constructed based on the synchrotron X-ray tomography data. We calculate the stress field in the composite and then quantitatively evaluate the kinetics of surface charge transfer and Li transport biased by mechanical stresses. We further model the cyclic behavior of the cell. The repetitive deformation of the active particles and the weakening of the interfacial strength cause gradual increase of the interfacial debonding. The mechanical damage impedes electron transfer, incurs more charge heterogeneity, and results in the capacity degradation in batteries over cycles.

25 ENERGY STORAGE↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2021

A Localized Cyber Threat Mitigation Approach For Wide Area Control of FACTS

We propose a localized oscillation amplitude monitoring (OAM) method for the mitigation of cyber threats directed at the wide area control (WAC) system used to coordinate control of Flexible AC Transmission Systems (FACTS) for power oscillation damping (POD) of active power flow on inter-area tie lines. The method involves monitoring the inter-area tie line active power oscillation amplitude over a sliding window. We use system instability - inferred from oscillation amplitudes growing instead of damping - as evidence of an indication of a malfunction in the WAC of FACTS, possibly indicative of a cyber attack. Monitoring the presence of such a growth allows us to determine whether any destabilizing behaviors appear after the WAC system engages to control the POD. If the WAC signal increases the oscillation amplitude over time, thereby diminishing the POD performance, the FACTS falls back to POD using local measurements. The proposed method does not require an expansive system-wide view of the network. We simulate replay, control integrity, and timing attacks for a test system and present results that demonstrate the performance of the OAM method for mitigation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Molecular Design Principles for Photosystem I-Based Biohybrid Solar Fuel Catalysts

Direct solar-to-chemical conversion offers a compelling route to clean, dispatchable energy. Photosystem I (PSI), an evolutionarily optimized light-driven oxidoreductase, can be repurposed for solar-fuel production by coupling its photochemistry to catalytic interfaces. However, the molecular determinants that govern productive electron transfer to abiotic catalysts remain poorly understood. Here, we present molecular structures of active PSI-Pt nanoparticle (PtNP) biohybrids that reveal how protein architecture controls catalyst access, binding geometry, and photocatalytic efficiency. Removal of stromal subunits exposes the electron transfer chain and enables PtNP binding proximal to the F X cluster, demonstrating that steric occlusion limits access to native acceptor regions in PSI. In contrast, in trimeric PSI, PtNPs bind at multiple sites per monomer, but only a subset are positioned within electron transfer distance of terminal cofactors, resulting in a heterogeneous population of productive and nonproductive configurations. Structural analyses and molecular dynamics simulations define the interface topology, electrostatics, and cofactor-to-nanoparticle distances that govern catalyst binding and electron transfer. These results establish that catalytic inefficiency arises not only from intrinsic electron transfer constraints but also from the distribution of binding geometries imposed by the protein scaffold. Together, these findings provide a molecular framework linking protein structure to biohybrid function and define design principles for engineering PSI-based solar fuel systems and protein-nanomaterial interfaces for light-driven catalysis.

biohybrid↗
DOE OSTI2023

Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data

=The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land-atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ~ 1/30 days -1 , 1/30 ~ 1/90 days -1 , and 1/90 ~ 1/365 days -1 ) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time-invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interconnection Cost Analysis in the PJM Territory

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data from interconnection studies for the PJM Territory, representing nearly 86% of all new unique generators requesting interconnection from 2000 to 2022. Project-level cost summary data are available for download on this page. We find: -Average interconnection costs have grown as the number of interconnection requests have escalated -Projects that have completed all required interconnection studies have the lowest cost compared to applicants still actively working through the interconnection process or those that have withdrawn. -Broader network upgrade costs are the primary driver of recent cost increase. -Potential interconnection costs for wind, storage, and solar are larger than for natural gas -Larger generators have greater interconnection costs in absolute terms, but economies of scale exist on a per kW basis. -Interconnection costs vary by location Berkeley Lab will publish a series of short analytical papers of generator interconnection costs to the transmission system for MISO, PJM, SPP, ISO-NE and NYISO, which you can find at https://emp.lbl.gov/interconnection_costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dispatching Grid-Forming Inverters in Grid-Connected and Islanded Mode

This paper explores the dispatchability of grid-forming (GFM) inverters in grid-connected and islanded mode. An innovative concept of dispatching GFM sources (inverters and synchronous generators) is proposed to output the target power by adjusting their droop intercepts. The fundamental principle is that the GFM inverter's active and reactive power is dictated by its frequency and voltage, and thus dispatching the active and reactive power of a GFM inverter can be achieved through dispatching its frequency and voltage. The concept is validated with an example microgrid system with two GFM inverters, one diesel generator, one GFL inverter, and the load in both grid-connected and islanded mode. This pioneering work results in practical guidance for the development of energy management systems for future electric grids with GFM and GFL inverters. The key findings are summarized as follows: 1) The GFM inverters can be dispatched through frequency and voltage droop intercepts to output the target power; 2) The dispatch rule for grid-connected and islanded mode is slightly different.

dispatch GFM inverter↗
DOE OSTI2025

Experimental Study of a Novel Semi-Transparent Bi-Facial Agri-Voltaic System: Initial Results

Solar (photovoltaic)-agriculture practices have experienced notable progress across several countries over the past few decades, creating a field widely known as agrivoltaics. This co-location practice has proved effective for certain crops that prefer partial shade like tomatoes, lettuce, and peppers, although it is less effective for crops needing more sun. Availability of natural sunlight with a specific spectral signature is necessary for agricultural practices as many plant species require an optimum-level and quality of sunlight for photosynthesis. The goal of this work is to model and validate ground-level irradiance for a semi-transparent bifacial solar collector equipped with a custom spectral reflector. By introducing intentional gaps between opaque solar cells, we hope to allow 50% of visible light to pass through the module. Additionally, this technology could reduce the soil temperature by 2° C, improve moisture retention, and enhance yield by providing partial solar radiation. In our model, we can calculate the total solar irradiance for the plants as a function of the cell distribution, material absorption, height of the panel and time of year with a spatial resolution of 0.0625 m2. When comparing theoretical estimates to experimental measurements of photosynthetically active radiation (PAR), we find a discrepancy of 32-48%, and a discrepancy of 19-38% after correction with TMY data.

McGraw, Duncan (ORCID:0000000256388118)↗
DOE OSTI2023

Forecasting for the Weather Driven Energy System - A New Task under IEA Wind

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast that has been the focus of considerable recent attention. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy has initiated a new IEA Wind Task with a much broader perspective, which includes prospective interaction with other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. In the new IEA Wind Task 51 (entitled "Foreacsting for the Weather Drive Energy System") the existing Work Packages (WPs) are complemented by work streams in a matrix structure. The Task is divided in three WPs according to the stakeholders: WP1 is mainly aimed at meteorologists, providing the weather forecast basis for the power forecasts. In WP2, the forecast service vendors are the main stakeholders, while the end users populate WP3. The new Task 51 started in January 2022. Planned activities include 4 workshops. The first will focus on the state of the art in forecasting for the energy system plus related research issues and be held during September 2022 in Dublin. The other three workshops will be held later during the 4-year Task period and address (1) seasonal forecasting with emphasis on Dunkelflaute, storage and hydro, (2) minute-scale forecasting, and (3) extreme power system events. The issues and conclusions of each of the workshops will be documented by a published paper. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective.

geophysics computing↗
DOE OSTI2023

Impact of Wildfires on Solar Generation, Reserves and Energy Prices

Wildfire seasons in the Western U.S. become more prolonged and intense in recent years bringing significant variability and uncertainty of solar generation. It greatly challenges the bulk power system and electricity market operation managed by California Independent System Operator (CAISO) as California leads both the wildfire records and the solar power integration. This study presents a screening-level analysis of the impact of wildfires on solar generation, operating reserve and energy prices applying historical real-world wildfire and market operation data. To the best of the authors knowledge, it is a first-of-its-kind study and will lay the foundation for market impact quantification and wildfire mitigation strategies design based on projected wildfire activities in future years.

electricity price↗
DOE OSTI2020

Influence of Nanoconfinement and Elevated Temperatures on Geocolloid-Facilitated Transport of Energy related Contaminants

Fossil fuels obtained using hydraulic fracturing and nuclear energy are deemed crucial for meeting the current and future energy demands of the United States. The utilization of these resources, especially fossil fuels from hydraulic fracturing, has increased rapidly within the last decade. However, with the increasing levels of hydraulic fracturing activities, the occurrence and fate of chemicals used in these processes in the geosystem, as well as the potential consequences of their interactions with environment and geosystems (e.g. mineral-based geocolloids and geosurfaces), have been increasingly recognized as issues warranting consideration owing to the toxic nature of these chemicals. Scientific results originated from this project has brought better understandings on how such contaminants can preferentially or adversely interact with geosystems and how geocolloids can facilitate their transport by realistically mimicking geological conditions and environments in the laboratory level for the first time. Based on such knowledge, it may eventually lead to the development of mitigation strategies for reducing and minimizing the transport and wide distribution of contaminants and pollutants. This aspect is important for the society to ensure that energy-related contaminants do not find their way to waterbodies that are used as drinking water or irrigation. The project is also anticipated to increase public awareness about potential adverse effects of energy systems on the geosphere and environment and may motivate society to consider better mitigation strategies to prevent such occurrences.

(Nano)Confinement↗
DOE OSTI2023

Chapter 11: Renewable Microgrids as a Foundation of the Future Sustainable Electrical Energy System

Renewable microgrids are an integral part of the future sustainable energy system. This chapter focuses on the role of renewable energy-based microgrids in the electricity system transformation. It is seen as a foundation that complements large-scale generation plants and high-voltage transmission lines by providing additional flexibility and resilience. This flexibility and resilience are crucial for balancing the energy system and ensuring its long-term sustainability, given the changing supply and demand patterns due to rooftop solar photovoltaics (PV), battery storage, electric mobility, and heat pumps. It is also an opportunity for residential and commercial consumers to become active participants in the energy market. Innovation, new technologies and business models will act as the key enabler for this transformation. The regulatory restructuring of the energy market will be crucial for successful integration of prosumers and aggregated energy communities. The chapter presents the vision of a sustainable, decentralized, and digitized energy future including market potential, most innovative case study projects and start-up companies, and the shifts needed in the regulatory landscape.

battery storage↗
DOE OSTI2023

Do Minor Interactions Trigger Star Formation in Galaxy Pairs?

Abstract We analyze the galaxy pairs in a set of volume limited samples from the Sloan Digital Sky Survey to study the effects of minor interactions on the star formation rate (SFR) and color of galaxies. We carefully design control samples of isolated galaxies by matching the stellar mass and redshift of the minor pairs. The SFR distributions and color distributions in the minor pairs differ from their controls at >99% significance level. We also simultaneously match the control galaxies in stellar mass, redshift and local density to assess the role of the environment. The null hypothesis can be rejected at >99% confidence level even after matching the environment. Our analysis shows a quenching in the minor pairs where the degree of quenching decreases with the increasing pair separation and plateaus beyond 50 kpc. We also prepare a sample of minor pairs with H α line information. We calculate the SFR of these galaxies using the H α line and repeat our analysis. We observe a quenching in the H α sample too. We find that the majority of the minor pairs are quiescent systems that could be quenched due to minor interactions. Combining data from the Galaxy Zoo and Galaxy Zoo 2, we find that only ∼1% galaxies have a dominant bulge, 4%–7% galaxies host a bar and 5%–10% of galaxies show active galactic nucleus (AGN) activity in minor pairs. This indicates that the presence of bulge, bar or AGN activity plays an insignificant role in quenching the galaxies in minor pairs. The more massive companion satisfies the criteria for mass quenching in most of the minor pairs. We propose that the stripping and starvation likely caused the quenching in the less massive companion at a later stage of evolution.

Astronomy & Astrophysics↗
DOE OSTI2023

Prompt Photofission Neutron Detection in Depleted Uranium

The detection of prompt photofission neutrons during active interrogation is a strong indication of the presence of special nuclear materials. However, the high-energy photons used for interrogation create a very challenging radiation environment for the detection of prompt fission signatures. These challenges include detector saturation and pulse pile-up. Additionally, there is an elevated neutron background that further challenges the detection of prompt fission neutrons. This background is produced because of (γ, Xn) photonuclear reactions in the surrounding high-Z materials. Here, we demonstrate the detection of prompt photofission neutrons in these challenging environments. Depleted uranium (DU) and lead targets are interrogated with bremsstrahlung photons produced by a 9-MV electron linear accelerator. Fast neutrons are detected with trans-stilbene organic detectors, and scintillation pulses are analyzed using a previously developed and demonstrated artificial neural network system. We observe a 5 times higher photoneutron count rate when the lead target is replaced with the DU target. Additionally, we observe a difference in the photoneutron light-output distributions of lead and DU. Here, this difference in the measured distributions is due to the difference in the photoneutron-energy spectra; DU photoneutrons are emitted with (γ, n) and watt-energy spectra, whereas lead photoneutrons are emitted with only the (γ, n) spectrum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗