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DOE OSTI2024

First Sagittarius A* Event Horizon Telescope Results. VII. Polarization of the Ring

Abstract The Event Horizon Telescope observed the horizon-scale synchrotron emission region around the Galactic center supermassive black hole, Sagittarius A* (Sgr A*), in 2017. These observations revealed a bright, thick ring morphology with a diameter of 51.8 ± 2.3 μas and modest azimuthal brightness asymmetry, consistent with the expected appearance of a black hole with mass M ≈ 4 × 106 M ⊙. From these observations, we present the first resolved linear and circular polarimetric images of Sgr A*. The linear polarization images demonstrate that the emission ring is highly polarized, exhibiting a prominent spiral electric vector polarization angle pattern with a peak fractional polarization of ∼40% in the western portion of the ring. The circular polarization images feature a modestly (∼5%–10%) polarized dipole structure along the emission ring, with negative circular polarization in the western region and positive circular polarization in the eastern region, although our methods exhibit stronger disagreement than for linear polarization. We analyze the data using multiple independent imaging and modeling methods, each of which is validated using a standardized suite of synthetic data sets. While the detailed spatial distribution of the linear polarization along the ring remains uncertain owing to the intrinsic variability of the source, the spiraling polarization structure is robust to methodological choices. The degree and orientation of the linear polarization provide stringent constraints for the black hole and its surrounding magnetic fields, which we discuss in an accompanying publication.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q3

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY25-Q4

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

VA Community Determinants of Health Data Curation Documentation FY26-Q1

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

VA Community Determinants of Health Data Curation Documentation FY26-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Community Determinants of Health (EDH) Data project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1 km grid) to another (e.g., U.S. Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, U.S. Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1 km grids. Some economic data may only be available at the ZIP code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., U.S. Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Community Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗

DOE OSTI2026

AI-Ready Control System for the Fermilab Accelerator Complex

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗

DOE OSTI2025

Learning broken symmetries with approximate invariance

Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data, due to asymmetries in the detector, or varying response resolution as a function of particle momentum. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry, effectively overconstraining the response of the neural network. We propose a learning model which balances the generality and asymptotic performance of unconstrained networks with the rapid learning of constrained networks. This is achieved through a dual-subnet structure, where one network is constrained by the symmetry and the other is not, along with a learned symmetry factor. In a simplified toy example that demonstrates violation of Lorentz invariance, our model learns as rapidly as symmetry constrained networks but escapes its performance limitations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Radiometric Testing of Germicidal UV Products, Round 2: Upper-Room Luminaires (CALiPER Report)

This report analyzes the independently tested performance of eight germicidal ultraviolet (GUV) upper-room luminaires marketed for use in occupied spaces and purchased between March and June 2023. This type of product is mounted to upper walls or ceilings to treat air in the portion of the room above occupants; this allows for safe use of the room when the device is operating, but requires sufficient air mixing between upper and lower portions of the room. Three of the luminaires used UV-emitting LEDs, and the remaining five luminaires used low-pressure mercury (LPM) lamps. Product testing covered radiometric and electrical performance for each luminaire. Initial performance was measured for all eight products, and four were additionally measured after 100 h and 500 h of operation. Measured performance data allowed for comparison against manufacturer or vendor claims if the tested products included such claims. Some products had no performance data available for a given quantity (e.g., UV-C output power), and only four of the eight luminaires had radiant intensity distribution data files in a standard format (e.g., IES LM-63) available for download from product websites. The lack of publicly available performance data makes it difficult for potential buyers and specifiers to identify suitable products and design GUV systems for their specific applications. When products had performance claims, they were sometimes contradictory (e.g., unexplained differences between multiple power values) or ambiguous (e.g., measurement units conflict with quantity, unclear whether luminaire power or lamp power, unclear whether UV output power or UV-C output power). Three of the eight tested luminaires had claimed output power (i.e., radiant flux) values that exceeded measured values by more than an order of magnitude. There was substantial variation in UV-C radiant efficiency, with a measured range of 0.3–1.9% for LED and 0.4–2.1% for LPM, as shown in Figure 1. For example, the LPM luminaire with 0.4% radiant efficiency would need 5 times the amount of electrical energy used by the LPM luminaire with 2.1% radiant efficiency to produce the same amount of UV-C output power. LPM luminaires that had parabolic reflectors aligned with inclined louvers exhibited substantially higher UV-C radiant efficiency than tested luminaires with other designs, potentially cutting energy use by 75%. These results indicate a substantial opportunity for more energy efficient LPM luminaire designs, while demonstrating that UV LED luminaires can offer comparable UV-C radiant efficiency in this application. This may seem surprising, given that LED emitters have lower UV-C radiant efficiency than LPM lamps, but the efficiency-throttling louvers that are generally required for LPM luminaires typically are not needed for LEDs thanks to their directionality. However, lateral beam angles (which describe beam width as viewed from above) were 41–83° for LED luminaires versus 89–110° for LPM luminaires. More luminaires may be required if their lateral beam angles are relatively small, and coverage may be poor if UV-C radiant intensity distribution (i.e., beam shape) is not considered when designing systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Search for supersymmetry using vector boson fusion signatures and missing transverse momentum in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

This paper presents a search for supersymmetric particles in models with highly compressed mass spectra, in events consistent with being produced through vector boson fusion. The search uses 140 fb −1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider. Events containing at least two jets with a large gap in pseudorapidity, large missing transverse momentum, and no reconstructed leptons are selected. A boosted decision tree is used to separate events consistent with the production of supersymmetric particles from those due to Standard Model backgrounds. The data are found to be consistent with Standard Model predictions. The results are interpreted using simplified models of R-parity-conserving supersymmetry in which the lightest supersymmetric partner is a bino-like neutralino with a mass similar to that of the lightest chargino and second-to-lightest neutralino, both of which are wino-like. Lower limits at 95% confidence level on the masses of next-to-lightest supersymmetric partners in this simplified model are established between 117 and 120 GeV when the lightest supersymmetric partners are within 1 GeV in mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Adaptable Standards for Discovery, Access, and Usability of Oak Ridge National Laboratory’s Data Portals and Catalogs

Oak Ridge National Laboratory (ORNL) is leveraging its established capabilities and subject matter expertise in data curation, governance, management, national security, and risk assessment and mitigation to support the US Department of Energy (DOE) Grid Modernization Initiative. Using standards modeled by the National Institute of Standards and Technology (NIST), the Data Curation Network (DCN), the Oak Ridge Leadership Computing Facility (OLCF), and other leading organizations in the fields of energy research, high-performance computing, and national and homeland security, ORNL seeks to provide a federated approach to research data discovery, use, and interoperability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis

Heterogeneous catalysis presents a distinct challenge for artificial intelligence (AI). Data sets are often small and inconsistently reported, catalyst representations are not standardized, and extracting fundamental knowledge requires integrating performance data, spectroscopic characterizations, and mechanistic models across multiple scales. Language offers a unifying representation across these modalities, making catalysis well suited for leveraging large language models (LLMs). By standardizing how catalytic data is represented, LLMs make dispersed experimental results more accessible to downstream statistical modeling. In this perspective, we focus our discussion around three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models. The discussion is followed by a perspective section on LLM-readiness of data, aligning LLM outputs with scientific correctness, and bridging lab-scale discovery to industrial deployment. Across each area, the most productive applications couple dispersed chemical knowledge with physics-grounded validation to produce verifiable hypotheses and actionable representations.

Catalysts↗

DOE OSTI2025

Learning Physically Interpretable Atmospheric Models From Data With WSINDy

The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by explicitly discovering partial differential equations governing atmospheric phenomena, identifying symbolic mathematical models with direct physical interpretations. The purpose of this paper is to demonstrate that, in particular, the weak-form sparse identification of nonlinear dynamics (WSINDy) algorithm can learn effective atmospheric models from both simulated and assimilated data. Our approach adapts the standard WSINDy algorithm to work with high-dimensional fluid data of arbitrary spatial dimension.

58 GEOSCIENCES↗

DOE OSTI2024

The MUSIC Critical Benchmark and Nuclear Data

The Measurement of Uranium Subcritical and Critical (MUSIC) experiment was a series of measurements of critical and subcritical configurations of bare highly enriched uranium. The goal was to compare measurement methods, analysis techniques, and simulation methods across regimes of criticality and to provide high-quality validation of 235 U nuclear data. A benchmark evaluation of the two critical configurations of the MUSIC experiments will soon be published in the release of the International Criticality Safety Benchmark Evaluation Project Handbook. The recent execution of the experiment aids in proper quantification of model simplifications and all uncertainties associated with the experiment. Historical benchmark evaluations are heavily relied on for uranium nuclear data validation despite the fact that the same level of documentation and comparable uncertainty analysis may not be present. The MUSIC evaluation is less likely to include “unknown unknowns” that could impede accurately modeling the system. Presented are both highly detailed and very simplified models, which represent the experimental configurations accurately, aiding the users of the benchmark for nuclear data or transport code validation. The sensitivities of k eff to nuclear data and nuclear data–related uncertainties are very similar between this experiment and previous bare uranium sphere experiments. In addition, the nuclear data uncertainties to any nuclides other than 235 U are small. For all these reasons, the recently evaluated MUSIC benchmark critical configurations could prove very useful for 235 U nuclear data validation. Currently, major libraries have good agreement with the experimental results, within 200 pcm for all nuclear data libraries, and within one standard deviation of the experimental result for most. Suggested nuclear data adjustments based on MUSIC and Lady Godiva are also presented, with posterior improvements to both the agreement in k eff and the uncertainty associated with the nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Cleaned 5-Minute Resolution Air Quality and Meteorological Data from Nine TCEQ CAMS Sites in Houston, Texas (Nov 2021 – Oct 2022)

These data encompass 5-minute air monitoring and meteorological observations collected in the greater Houston, Texas metropolitan region, at nine (9) Continuous Ambient Monitoring Stations (CAMS) operated by the Texas Commission on Environmental Quality (TCEQ) between November 1, 2021 and October 31, 2022. The CAMS sites (CAMS 1, 8, 35, 45, 148, 403, 405, 410, and 1052) were chosen because their instrumentation includes measurements of PM2.5. These sites also provide continuous multi-parameter air-quality and meteorological measurements. Particulate matter (PM2.5, PM10) was sampled along with several trace gases, including ozone (O3), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and carbon monoxide (CO). The data set also contains standard surface meteorological parameters (temperature, humidity, pressure, wind speed, and wind direction). Several sites also include AutoGC-based measurements of volatile organic compounds (VOCs). Air monitoring instruments deployed at the selected sites comprise the following systems: BAM-1020 or TEOM (PM2.5), Thermo Scientific TEI 49i (O3), TEI 42i (NOx), and AutoGCs (VOCs). This data set is similar to the data included within the houairq5mX1.00 datastream, except for a few additional quality control steps. A systematic data cleaning and verification process was performed on the data set to ensure its quality and preparation for analysis. Removal of non-numeric status flags (e.g., [LIM], [QAS], [SPZ], [CAL], [PMA], [AQI], [SPN], [MAL]) was accomplished by employing rule-based string parsing to extract valid numerical values. Missing entries were set to -9999; however, invalid or anomalous values (e.g., 99999) were retained as originally reported by the TCEQ to preserve data provenance. The time sequence was verified for completeness, removal of duplicates, and uniformity at 5-minute intervals. Column labeling was standardized, and corresponding values were assessed for physical plausibility. All timestamps in the data set were reported in Coordinated Universal Time (UTC) as provided by the TCEQ. Further, the latitude and longitude coordinates were added for each CAMS site. A subset of the data (June 1–September 30, 2022) has been used in the following publication: Subba et al. 2025. “Implications of sea breeze circulations on boundary layer aerosols in the southern coastal Texas region.” EGUsphere 2025: 1–49, https://doi.org/10.5194/egusphere-2025-2659.

latitude↗

DOE OSTI2026

FC Site 4.2 - NLR Scanning Lidar (Halo XR+ 199) Radial Wind Speed Statistics / Derived Data

This dataset contains statistics (mean and standard deviation) calculated from quality-control lidar radial wind speed data through the LiSBOA method. This is an intermediate product for the synthesis of dual-Doppler wind maps.

17 WIND ENERGY↗