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We compute bounds on coefficients of effective operators in the Standard Model that can be inferred from observations of neutrino scattering by the COHERENT experiment. While many operators are bound extremely well by past experiments the full future data set from COHERENT will provide modest improvements for some operators.
Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.
This report summarizes the progress of SFR metallic fuel qualification related activities, which are focused on providing quality assurance relevant information applicable to experiments irradiated during the Integral Fast Reactor (IFR) program. An overview of the metallic fuel performance data and the associated databases, including the EBR-II Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT Experimental Relational Database (TREXR) is included. The legacy data in the databases, including as-built, post-irradiation examination (PIE), operating parameters, and out-of-pile experiment post-test data are introduced. The SFR metallic fuel Quality Assurance Program Plan (QAPP) and its implementation to qualify these legacy data is described in detail. Important PIE data QA documents and the specifications of seven types of PIE measurements (contact profilometry, laser profilometry, neutron radiography, gamma scan, fission gas release fission gas chemistry, and metallography) are provided. Examples of the implementation of the QAPP to qualify each of those types of PIE data are provided.
The Advanced Reactor Terminology Graphite Research and Development program is currently measuring irradiated material property changes in several grades of nuclear graphite to predict behavior and operating performance within the core of these new high temperature reactor designs. The Advanced Graphite Creep (AGC) experiment, consisting of six irradiation capsules, will generate the irradiated graphite performance data for the Very High Temperature Reactor operating conditions. All six capsules in the experiment conducted at Idaho National Laboratory will be irradiated in the Advanced Test Reactor, disassembled in the Hot Fuel Examination Facility, and examined at the Idaho National Laboratory Research Center. This is the disassembly report describing the disassembly, shipment, post irradiation inspection, and storage of the graphite specimens contained within the AGC 4 irradiation test series capsule (the fourth irradiation capsule of the series). AGC 4 was irradiated in the Advanced Test Reactor (ATR) East Flux Trap (EFT) during ATR Cycle 157D, 158A, 162A, 162B, 164A, 164B, 166A, and Cycle 166B. Approximately 3.6 dpa was achieved. Desired experiment temperatures were exceeded by at least 100C during the second Cycle of irradiation due to the insertion of the KJRR experiment. The capsule was removed from the ATR and transferred to the Hot Fuel Examination Facility on May 15, 2020 and eventually unloaded into the Hot Fuel Examination Facility (HFEF) Decon Cell through Penetration 2D on February 26, 2021. It was moved to the HFEF Main Cell Window 3M for disassembly on March 15, 2021. Disassembly and specimen extraction began March 18, 2021, and packaging of the graphite specimens was completed on April 16, 2021. Several anomalies were noted, specifically that the radiological dose rates were nominally an order of magnitude higher than that of the previous AGC experiments. This report summarizes the disassembly of the AGC 4 experiment.
The Advanced Reactor Terminology Graphite Research and Development program is currently measuring irradiated material property changes in several grades of nuclear graphite to predict behavior and operating performance within the core of these new high temperature reactor designs. The Advanced Graphite Creep (AGC) experiment, consisting of six irradiation capsules, will generate the irradiated graphite performance data for the Very High Temperature Reactor operating conditions. All six capsules in the experiment conducted at Idaho National Laboratory will be irradiated in the Advanced Test Reactor, disassembled in the Hot Fuel Examination Facility, and examined at the Idaho National Laboratory Research Center. This is the disassembly report describing the disassembly, shipment, post irradiation inspection, and storage of the graphite specimens contained within the AGC 4 irradiation test series capsule (the fourth irradiation capsule of the series). AGC 4 was irradiated in the Advanced Test Reactor (ATR) East Flux Trap (EFT) during ATR Cycle 157D, 158A, 162A, 162B, 164A, 164B, 166A, and Cycle 166B. Approximately 3.6 dpa was achieved. Desired experiment temperatures were exceeded by at least 100C during the second Cycle of irradiation due to the insertion of the KJRR experiment. The capsule was removed from the ATR and transferred to the Hot Fuel Examination Facility on May 15, 2020 and eventually unloaded into the Hot Fuel Examination Facility (HFEF) Decon Cell through Penetration 2D on February 26, 2021. It was moved to the HFEF Main Cell Window 3M for disassembly on March 15, 2021. Disassembly and specimen extraction began March 18, 2021, and packaging of the graphite specimens was completed on April 16, 2021. Several anomalies were noted, specifically that the radiological dose rates were nominally an order of magnitude higher than that of the previous AGC experiments. This report summarizes the disassembly of the AGC 4 experiment.
We present the design and test results of a novel data transmitter ASIC operating up to 20.48 Gbps with 4-level Pulse-Amplitude-Modulation (PAM4) for particle physics experiments. This ASIC, named GBS20, is fabricated in a 65 nm CMOS technology. Two serializers share a 5.12 GHz Phase Locked Loop (PLL) clock. The outputs from the serializers are combined into a PAM4 signal that directly drives a Vertical-Cavity-Surface-Emitting-Laser (VCSEL). The input data channels, each at 1.28 Gbps, are scrambled with an internal 2$^{7}$-1 Pseudo-Random Binary Sequence (PRBS), which also serves as a frame aligner. GBS20 is tested to work at 10.24 and 20.48 Gbps with a VCSEL-based Transmitter-Optical-Subassembly (TOSA). The power consumption of GBS20 is below 238 mW and reduced to 164 mW in the low-power mode.
This report documents the quantitative results of the causal common-cause failure (CCF) parameter estimations for the failure cause groups “component,” “design,” “environment,” “human,” and “other,” based on CCF data through 2020 in the U.S. Nuclear Regulatory Commission (NRC) CCF database: https://rads.inl.gov/Pages/CCF.aspx. This report utilizes the same data period (2006–2020) and CCF templates as INL/EXT-21-62940, Revision 1, CCF Parameter Estimations, 2020 Update. The 2015 causal CCF prior distributions for the specific failure cause groups (instead of the 2015 generic CCF prior distributions) were used in this report to estimate the associated causal CCF parameters. All the 2015 causal CCF prior distributions and generic CCF prior distributions were developed in INL/EXT-21-43723, Developing Generic Prior Distributions for Common Cause Failure Alpha Factors and Causal Alpha Factors, using CCF data from 1997 to 2015. These quantitative results were developed to support the causal alpha factor model and should be used as appropriate in probabilistic risk assessment (PRA) studies such as the NRC Significance Determination Process for commercial nuclear power plants in the United States.
Advanced reactors, such as the molten salt reactor (MSR), require materials that will withstand harsher environments than the materials used for lower temperature water reactors. Materials used in MSR construction need not only withstand elevated temperature, temperature cycling, and neutron radiation, but must be able to withstand the corrosive molten salt environment. Significant materials research has been driven by the materials needs of the MSR due to the harsh environment and the material data requirements to support licensing. Limited operation experience with MSRs has made this challenging work. Information is very limited on materials degradation due to irradiation, molten salt corrosion, elevated temperature, and the resulting fatigue, creep, and creep-fatigue loading during operation. While efforts are underway to better understand the effects of this harsh environment on construction materials, the Advanced Reactors Technologies (ART) Program has been working to develop materials surveillance test articles that could be used in a materials surveillance program and allow for the collection of information on the materials degradation during plant operation and could support timely licensing of these advanced reactors.
Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95 , β p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. Furthermore, these results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data.
High-energy physics (HEP) experiments demand data acquisition (DAQ) systems capable of orchestrating complex detector operations, high data throughput, and responsive, real-time feedback. Traditional systems often have steep learning curves, making onboarding difficult for new users. The Off-The-Shelf Data Acquisition $otsdaq$ framework was developed to address these issues by providing a modular and flexible interface that is easier to operate while remaining customizable enough for experimental setups. As the upcoming Mu2e experiment prepares for deployment, improving stability, usability, and performance has become increasingly critical. Our work enhances $otsdaq$ with features that streamline visualization, correct data metrics, improve debugging workflows, and stabilize the user interface.
We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the deployment of configurable, private, and extensible agents that retrieve and reason over them. An instance of Archi has been deployed for the Computing Operations team of the CMS experiment at CERN's LHC since February 2026 as a support agent for technical operators, offering retrieval and analysis capabilities by combining documentation, historical data, and live monitoring systems. We evaluate the system on operator feedback and a question set collected from production usage, graded by human and automated panels. The system proves effective at operational tasks, resolving real-world queries posed by CMS operators. We also observe that locally-hosted, open-weight models perform competitively, enabling fully private management of sensitive data.
The nuclear industry is rapidly developing many advanced-reactor concepts for near-term deployment in both traditional and non-traditional nuclear-powered applications. One such category of advanced reactor is the microreactor, a class of reactor with less than 20MWth power output, intended for applications where the economics or logistics of traditional power sources are difficult. This includes applications such as remote communities, mining sites, defense installations, or humanitarian and disaster-relief missions. One key enabling feature for the successful deployment of microreactors is a remote operations capability. Remote operations provide monitoring and control capabilities which can significantly reduce staffing costs by eliminating the need for licensed operators at each reactor facility and improve the economic viability for microreactor deployment. A remote concept of operations is not currently an established capability in the nuclear industry. In addition, no demonstration microreactor is expected to complete construction or go critical until at least 2026. This leaves two major capability gaps: the successful demonstration of a remote concept of operations for microreactors and a test bed suitable for said demonstration. Both gaps must be addressed in order to advance the remote concepts of nuclear operation and, more broadly, microreactors themselves from paper to reality. This paper aims to fill these gaps and describes a test bed that would support development and deployment of a remote concept of nuclear operations, initial experimental results from that test bed, and the application of the test bed and experimental results for a digital-twin-based remote concept of operations underdevelopment at Idaho National Laboratory (INL). The platform chosen as a remote concept of nuclear operations test bed is the Single Primary Heat Extraction and Removal Emulator, known as SPHERE, located at INL. SPHERE is a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The small-scale and non-nuclear nature of SHPERE limit safety concerns associated with remote operations while still providing the physical response representative of a microreactor. A network connection was added to SPHERE that enables remote-monitoring capability. This allows for real-time data streaming to networked workstations, data historians, and human-machine interfaces (HMIs). These are all critical components in a remote concept of operations, thus providing a robust development and demonstration platform. An initial experiment was performed using the SPHERE remote operations testbed. This included running a comprehensively instrumented SPHERE through a series of steady-state and transient operating scenarios in both normal and abnormal operating conditions, all while streaming live test data to a remote HMI and data warehouse. This initial experiment served three purposes: (1) characterizing the response of SPHERE, (2) demonstrating the remote connection to SPHERE, and (3) providing a baseline data set for development of a digital-twin-based remote concept of operations that is under development at INL.
The nuclear industry is rapidly developing many advanced-reactor concepts for near-term deployment in both traditional and non-traditional nuclear-powered applications. One such category of advanced reactor is the microreactor, a class of reactor with less than 20MWth power output, intended for applications where the economics or logistics of traditional power sources are difficult. This includes applications such as remote communities, mining sites, defense installations, or humanitarian and disaster-relief missions. One key enabling feature for the successful deployment of microreactors is a remote operations capability. Remote operations provide monitoring and control capabilities which can significantly reduce staffing costs by eliminating the need for licensed operators at each reactor facility and improve the economic viability for microreactor deployment. A remote concept of operations is not currently an established capability in the nuclear industry. In addition, no demonstration microreactor is expected to complete construction or go critical until at least 2026. This leaves two major capability gaps: the successful demonstration of a remote concept of operations for microreactors and a test bed suitable for said demonstration. Both gaps must be addressed in order to advance the remote concepts of nuclear operation and, more broadly, microreactors themselves from paper to reality. This paper aims to fill these gaps and describes a test bed that would support development and deployment of a remote concept of nuclear operations, initial experimental results from that test bed, and the application of the test bed and experimental results for a digital-twin-based remote concept of operations underdevelopment at Idaho National Laboratory (INL). The platform chosen as a remote concept of nuclear operations test bed is the Single Primary Heat Extraction and Removal Emulator, known as SPHERE, located at INL. SPHERE is a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The small-scale and non-nuclear nature of SHPERE limit safety concerns associated with remote operations while still providing the physical response representative of a microreactor. A network connection was added to SPHERE that enables remote-monitoring capability. This allows for real-time data streaming to networked workstations, data historians, and human-machine interfaces (HMIs). These are all critical components in a remote concept of operations, thus providing a robust development and demonstration platform. An initial experiment was performed using the SPHERE remote operations testbed. This included running a comprehensively instrumented SPHERE through a series of steady-state and transient operating scenarios in both normal and abnormal operating conditions, all while streaming live test data to a remote HMI and data warehouse. This initial experiment served three purposes: (1) characterizing the response of SPHERE, (2) demonstrating the remote connection to SPHERE, and (3) providing a baseline data set for development of a digital-twin-based remote concept of operations that is under development at INL.
The Advanced Photon Source (APS) has recently completed a major upgrade, replacing its 25-year-old storage ring with a cutting-edge hybrid seven-bend achromat lattice enhanced by six additional reverse bends. The new design achieves a natural emittance of 42 pm-rad, enabling the production of X-rays up to 500 times brighter than those generated by the original APS. A key innovation of the upgrade is the implementation of a swap-out injection scheme, which replaces entire depleted bunches instead of performing traditional top-up injection. This approach enables on-axis injection to accommodate for the reduced dynamic aperture resulting from strong focusing. This paper outlines the commissioning process, shares initial operating experience with swap-out injection, and presents performance data for new systems such as the bunch-lengthening cavity.
There is an increasing aspiration to utilize machine learning (ML) for various tasks of relevance to national security. ML models have thus far been mostly applied to tasks and domains that, while impactful, have sufficient volume of data. For predictive tasks of national security relevance, ML models of great capacity (ability to approximate nonlinear trends in input-output maps) are often needed to capture the complex underlying physics. However, scientific problems of relevance to national security are often accompanied by various sources of sparse and/or incomplete data, including experiments and simulations, across different regimes of operation, of varying degrees of fidelity, and include noise with different characteristics and/or intensity. State-of-the-art ML models, despite exhibiting superior performance on the task and domain they were trained on, may suffer detrimental loss in performance in such sparse data environments. This report summarizes the results of the Laboratory Directed Research and Development project entitled Trust-Enhancing Probabilistic Transfer Learning for Sparse and Noisy Data Environments. The objective of the project was to develop a new transfer learning (TL) framework that aims to adaptively blend the data across different sources in tackling one task of interest, resulting in enhanced trustworthiness of ML models for mission- and safety-critical systems. The proposed framework determines when it is worth applying TL and how much knowledge is to be transferred, despite uncontrollable uncertainties. The framework accomplishes this by leveraging concepts and techniques from the fields of Bayesian inverse modeling and uncertainty quantification, relying on strong mathematical foundations of probability and measure theories to devise new uncertainty-aware TL workflows.
Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.
The NML cryogenic plant cools two individually cryostated superconducting radio frequency (SRF) capture cavities and one prototype ILC cryomodule with eight SRF cavities. This complex accelerates electrons at 150 MeV for the Integrable Optics Test Accelerator (IOTA) ring, located at the Fermilab Accelerator Science and Technology (FAST) facility. The cryogenic plant is composed of two nitrogen precooled Tevatron satellite refrigerators, two Mycom 2016C compressors, a cryogenic distribution system, a Frick purifier compressor, two charcoal bed adsorber purifiers, and a liquid ring vacuum pump with a roots booster. The SRF cavities are immersed in a 2.0 K liquid helium bath, shielded with a 5 K gaseous helium shield and a liquid nitrogen cooled thermal shield. Since 2019, this R&D accelerator complex has gone through four science runs with an average duration of 12 months. Operational experience for each run, availability metrics, performance data and common outages are presented in this paper.
The Production and Distributed Analysis (PanDA) system is a data-driven workload management system engineered to operate at the LHC data processing scale. The PanDA system provides a solution for scientific experiments to fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, and robustness. The system has successfully proven itself through nearly two decades of steady operation in the ATLAS experiment, addressing the intricate requirements such as diverse resources distributed worldwide at about 200 sites, thousands of scientists analyzing the data remotely, the volume of processed data beyond the exabyte scale, dozens of scientific applications to support, and data processing over several billion hours of computing usage per year. PanDA’s flexibility and scalability make it suitable for the High Energy Physics community and wider science domains at the Exascale. Beyond High Energy Physics, PanDA’s relevance extends to other big data sciences, as evidenced by its adoption in the Vera C. Rubin Observatory and the sPHENIX experiment. As the significance of advanced workflows continues to grow, PanDA has transformed into a comprehensive ecosystem, effectively tackling challenges associated with emerging workflows and evolving computing technologies. The paper discusses PanDA’s prominent role in the scientific landscape, detailing its architecture, functionality, deployment strategies, project management approaches, results, and evolution into an ecosystem.