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DOE OSTI2026

Transforming jet flavour tagging at ATLAS

Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.

Characterization and analytical techniques↗

DOE OSTI2020

Simplified Two-Stage Model Predictive Control for a Hybrid Multilevel Converter With Floating H-Bridge

This paper proposes a simplified two-stage model predictive control (ST-MPC) for a hybrid multilevel converter, which is an active-neutral-point-clamped converter with floating H-bridge (ANPC-H). The objective of the first stage is to select the voltage vector that has the optimal current tracking performance by using a novel geometrical positioning approach in the complex plane. The second stage selects the best switching state among all the available switching states that belong to the same voltage vector obtained in the first stage, to balance dc capacitor voltages and reduce the common mode voltage. The proposed ST-MPC can dramatically reduce the computational burden and ensure the best current tracking by the two-stage structure, such that the execution time is much shorter compared with the conventional MPC. In addition, the geometrical positioning approach in the first stage is generic and can be applicable for any multilevel converters with N-level output; thus, this ST-MPC can be applied for both sevenand nine-level operation of the hybrid ANPC-H converter under different dc voltage ratios. Both simulation results and experimental results obtained on a silicon carbide hybrid ANPC-H converter prototype validate the feasibility and effectiveness of the proposed ST-MPC strategy.

42 ENGINEERING↗

DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗

DOE OSTI2023

Utah FORGE 3-2417: Fiber-Optic Geophysical Monitoring of Reservoir Evolution - Workshop Presentation

This is a presentation on the Fiber-Optic Geophysical Monitoring of Reservoir Evolution at the Utah FORGE Milford Site project by Rice University, presented by Prof. Jonathan Ajo-Franklin. The project's objective was to develop an end-to-end fiber-optic sensing approach for EGS to track the initial zone of fracture creation, zones of connected mechanically compliant fractures, and zones of flowing fractures linking the injection/production well pair. This approach would also be used to integrate data into an improved thermo-hydro-mechanical model (THM). This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Flow of the normal component of He-II about bluff objects observed with He$^*_2$ excimers

Flow of the normal fluid component of He II about a cylinder and flat plate in a channel with a square cross-section was observed by tracking clouds of He$^*_2$ excimers. Flow was produced using a lithographically patterned heater. The direction and speed of flow projected onto a plane parallel to the axis of the channel and perpendicular to the heater exhibited significant change with time even after the heater was turned off. Flow was recorded in movies. Excimers moved at least as fast as could be recorded by the camera. Velocity vector field maps of flow suggest formation of structures downstream of the cylinder object that are consistent with parts of eddies. Here, in this paper, we establish a foundation to observe normal component flow of a quantum fluid over centimeters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Evaluation of Saccadic Component Measure on Smooth Pursuit Tests

ABSTRACT Introduction Despite the advancement of eye-tracking technology for smooth pursuit (SP) eye movement evaluation, qualitative observation offers much information that is not captured by computers; hence, both objective and qualitative information should be utilized to evaluate SP. This study examined the consistency among our clinicians when evaluating SP using normal (N), grossly normal (GN), mildly abnormal (MA), and abnormal (AB) as classifications. We then evaluated the effect of combining GN and MA into a single subclinical (SUBC) category. We also evaluated the computerized percent saccade (PS) metric by determining its sensitivity and specificity in classifying SP. Materials and Methods Retrospective horizontal and vertical SP test videos and numerical data for 70 participants were obtained from the Neuro Kinetics Neuro-Otologic Test Center and de-identified. From this, eye-tracking videos, time plots of eye-tracking positional data, and tables of SP eye-tracking performance data were generated for 0.1, 0.3, and 0.5 Hz in both horizontal and vertical planes, totaling 6 tests per subject. Three clinicians rated each subject’s SP performance as N, GN, MA, or AB for a total of 6 ratings (3 frequencies, horizontal and vertical). This process was repeated using N, SUBC, and AB as rating categories. Clinicians also provided an overall SP rating for each plane as follows: AB if the results were abnormal for 2 or more frequencies tested. Alternatively, if fewer than 2 frequencies presented with a rating of AB, then an overall rating of MA, GN, or N was determined at the respective clinician’s discretion. Results When the 3 clinicians were tasked with classifying SP videos using 4 clinical categories, fair overall agreement was demonstrated. However, when MA and GN categories were combined into an SUBC category, the overall agreement for the 3 clinicians improved slightly for both horizontal SP (HSP) and vertical SP (VSP). This pattern of agreement did not differ considerably when comparing HSP versus VSP, and good consistency and reliability was observed across clinicians. Again, inter-rater consistency was smaller for VSP versus HSP despite the reduction in clinical categories. Cut-off values were generated for the PS metric and demonstrated good specificity and sensitivity when they were exceeded for 2 or more frequencies in a particular plane when evaluating a subject’s SP test. Conclusions

General & Internal Medicine↗

DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗

DOE OSTI2022

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the “glue” that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5 T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Herein our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Distributed model predictive control for coordinated, grid-interactive buildings

Continued focus on reducing carbon emissions and improving energy efficiency requires buildings to become grid-interactive and not just behave as static consumers. A distributed model predictive control (DMPC) algorithm known as Limited-Communication (LC) DMPC is modified to enable grid-interactive buildings. A grid-aggregator subsystem is added that allows for a bulk grid power reference signal to be followed while the individual building subsystems also achieve their local comfort objectives. The LC-DMPC algorithm is applied for the first time to systems with multiple buildings. Adequate power tracking is shown for different simulation scenarios involving heterogeneous buildings, and next steps are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy

Abstract Objective . To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach . Our model provides for selection of preferred ID parameters ( I p ) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I p and macroscopic RTP. Selection of I p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen I p : N k , k = 2, 3, …, 10, the number of ionizations in clusters of k or more per particle, and F k , k = 1, 2, …, 10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results . The preferred I p were N 4 and F 5 for aerobic cells, N 5 and F 7 for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferred N k . Conversely, there was no significant difference for F 5 for aerobic cells and F 7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Significance . Our model provides a practical means to select preferred I p from radiobiological data, and to convert I p to the macroscopic cluster dose for particle RTP.

Engineering↗

DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

This paper uses a two-level model predictive control-based approach for the coordinated control and energy management of an integrated system that includes photovoltaic (PV) generation, energy storage, and building loads. Novel features of the proposed local controller include (1) the ability to simultaneously manage building loads and energy storage to achieve different operational objectives such as energy efficiency, economic cost efficiency, demand response and grid optimization through the design of specific power trajectory tracking performance functionals, (2) an energy trim function that minimizes the impact of solar forecasting errors on system performance, and (3) the design of a state of charge controller that uses day-ahead forecast of solar power and building loads to intialize energy storage at the start of each day. The local controller is tested in simulation using an exemplary system with PV generation, energy storage and dispatchable building loads. Two sample days with different PV forecasts and multiple case scenarios are considered, and the performance of the algorithm in managing the real and reactive net building load trajectories and the ramp rate of PV injections into the utility network are evaluated. The simulations are based on actual forecasted and measured PV data, and the results show that the local controller meets the tracking requirements for real and reactive power within the operating constraints of the building.

14 SOLAR ENERGY↗

DOE OSTI2020

Acquisition and Implementation of a Comprehensive Environmental Permits Linking Tool at Savannah River Site - 20234

Historically, Savannah River Nuclear Solutions LLC (SRNS) tracked environmental regulatory commitments and the requirements from hundreds of permits at the Savannah River Site (SRS) using several separate methods, making integrated compliance assurance cumbersome and labor-intensive. When SRNS experienced an increase in environmental issues in 2017, SRNS management and U.S. Department of Energy - Savannah River (DOE-SR) management decided a single, proactive approach was needed to capture environmental permit information (including regulations, Consent Orders, DOE Orders, and any other state or federally issued statement of requirements), track the tasks necessary to ensure compliance with these requirements, and thereby mitigate the risk of noncompliance. SRNS developed a list of mandatory objectives that the tool must meet to function as a Comprehensive Environmental Permits Linking Tool (CEPLT). A key requirement was the ability to map Site permits to their governed locations and display the associated requirements at the compliance point (e.g., outfall, stack, waste unit, etc.). Several options included modifying existing onsite resources, building a custom onsite solution, purchasing an off-the-shelf solution, and contracting an offsite developer to build a custom solution. SRNS concluded an off-the-shelf solution with configuration and customization options would provide the flexibility to fit the unique needs of Savannah River Site (SRS) while taking advantage of industry-tested software and providing a reduced deployment timeline. SRNS chose Gensuite{sup R} a, a cloud-based solution that offers numerous a la carte applications in the environmental, health, and safety arenas, as the best candidate. SRNS selected three (3) integrated applications (Compliance Calendar, Permit Manager, and Mapper) to function as the CEPLT. The Compliance Calendar module allows for creation and tracking of regulatory commitment tasks assigned to responsible environmental professionals. Permit Manager organizes permits and other requirement documents, linking the commitments in each to Compliance Calendar tasks and/or implementing procedures. Mapper provides GIS capability for mapping the data from the other two modules to their physical onsite locations. The CEPLT was configured to allow for other SRS Site Tenants to eventually utilize the applications. Additionally, DOE-SR uses the CEPLT to provide an overview of contractor environmental compliance activities and to organize DOE-specific documents and tasks. The CEPLT fulfilled the requirement of meeting current compliance needs as well as providing the ability to grow as new organizations are incorporated and functionality is expanded. SRNS now uses the CEPLT to more effectively manage permit requirements, improve knowledge transfer, and increase the Site's overall protection of the environment, the Site worker, and the public. Possible future uses of the system include integration of mobile applications for timely communication of potential non-compliant conditions and DOE complex-wide deployment allowing for enhanced DOE site and Head Quarters oversight. Implementation of CEPLT will result in cost savings, both in terms of dollars and man-hours. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Satellite Enveloped with STITCHED Engineering Sensors for Detection of Approaching Objects

Today as well as tomorrows spaceborne assets impact almost all areas of national and nuclear security. Spaceborne assets can not only collect and disseminate valuable data, well beyond just the visual, but also track terrestrial-based mobile assets in real-time, and active spaceborne platforms potentially pose serious risk to vulnerable earth-based systems and infrastructures. The capability to defend national spaceborne assets from attack/interference is critical for security interests. This effort supports this mission through the cost-effective preeminent detection of approaching threats to our nation’s vital resources, in order to help secure and trust these high-value assets against the threats of tomorrow. This project develops novel fabrication techniques for conformal, low-profile and lightweight leakywave antenna (LWA) detection/imaging systems, which fuses technical embroidery (TE) and laser ablation (LA) processes with LWA design. Technical embroidery is an emerging field in additive textile manufacturing where flexible materials and functionalized fabrics are created for a wide variety of uses and purposes, while laser ablation is the process of removing material from a solid surface by irradiating it with a laser beam. Here, thin, conformal antenna designs are designed, modeled and fabricated using both TE and LA, to create lightweight, flexible and conformal object detection and imaging radars. This novel development ensures our nation’s ability to field advanced lightweight and conformal technologies to protect spaceborne assets.

42 ENGINEERING↗

DOE OSTI2025

Enhanced Lighting Signals for Safety and Efficiency - Experiments With Addressable LEDs

For enhanced roadway safety, clear and immediate visual cues are essential for preventing accidents between drivers and pedestrians. At intersections, however, the line of sight to other roadway users may be obstructed by vehicles and infrastructure. Additionally, adverse conditions, including low visibility, poor weather, or inadequate lighting can increase the potential for collisions. Distracted drivers and pedestrians can further exacerbate the risk of accidents, particularly when using a smartphone, rather than focusing on roadway surroundings. These issues demonstrate the need for infrastructure upgrades that enhance visibility and awareness at crosswalks. A potential solution is through enhanced lighting signals integrated into the roadway infrastructure. One such example is the use of addressable LEDs, individually controllable lights that can change color and brightness instantaneously through programmable microcontrollers. They can be installed and integrated into crosswalks to maintain visibility in conditions where pedestrians may be difficult to see, while also offering peripheral cues to pedestrians who may be distracted by their phones or other objects rather than the road. Such a system (as one example) that is integrated into a traffic intersection digital twin that tracks all roadway users accurately, has the potential to enhance visibility of vulnerable road users, and thus enhance safety. This paper examines the potential implementation and feasibility of this technology, as well as the safety benefits it could provide. Laboratory experiments with addressable LEDs reveal the capabilities and challenges of this technology for roadway infrastructure safety. These findings could pave the way for more integrated lighting in infrastructure for vehicles and pedestrians at intersections, merge and diverge locations, and other areas where complex interactions present safety hazards. Such lighting solutions, enabled by modern computation and communications, could enhance safety and efficiency in our transportation system and improve overall mobility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Enhanced Lighting Signals for Safety and Efficiency - Experiments with Addressable LEDs

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Tracktable

Tracktable is a toolkit for analysis and visualization of the trajectories of moving objects. Its main focus is on air and sea traffic. It can also work with more abstract trajectories such as eye tracking data. We supply a set of core data structures, input/output routines, math and machine learning algorithms that operate on those data structures, and visualization algorithms to produce movies and still images of the results.

Wilson, Andrew↗