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DOE OSTI2024

Regulatory Treatment of Low Frequency External Events under a Risk-Informed Performance-Based Licensing Pathway: Enhanced SPRA-based Margins Assessment

Recently there has been development in the field of risk-informed performance-based (RIPB) design and licensing approaches, which leverage detailed risk assessments and performance-based metrics to allow flexibility and innovation. These RIPB approaches include the probabilistic treatment of external hazards, including low frequency events that are beyond the design basis. However, there are certain challenges that have been identified to the probabilistic treatment of low frequency external events, primarily due to uncertainty in the hazard curve and the associated plant response to rare, severe events. The NRC is currently developing 10 CFR Part 53 that would establish a technology-inclusive regulatory framework for use by applicants for new commercial advanced nuclear reactors. By examining the regulatory safety criteria contained within draft 10 CFR Part 53 and associated draft RIPB seismic design guidance, potential challenges were identified in demonstrating satisfaction of the safety criteria for low frequency external events, with specific difficulties associated with demonstrating compliance with the quantitative health objectives (QHOs). Non-LWRs are expected to utilize the direct calculation of offsite consequence, rather than use surrogates, for comparison to the QHOs, which can be particularly challenging as the previously identified uncertainties are compounded by uncertainties in the response of the neighboring population. The central recommendation from this effort is that it is necessary to develop an approach for demonstrating compliance with the safety criteria in draft Part 53 that addresses the key challenges while providing flexibility to applicants. This paper summarizes key findings, establishes a series of high-level goals, and reviews a newly developed approach to address the major challenges associated with assessing compliance with QHOs, with avenues to demonstrate compliance based on either the estimated consequence or the available margin to event occurrence, while also building on existing experience of seismic margins assessments. The paper also provides examples to demonstrate the application of the approach, as well as recommendations and potential future work.

external hazards↗
DOE OSTI2026

Development of a pattern recognition algorithm for reconstructing multi-particle events in the Mu2e detector

Mu2e is an upcoming experiment at Fermilab and its main goal is to search for the Charged Lepton Flavor Violation (CLFV) in the coherent transition of a muon into an electron on an Al target. In Mu2e, multi-particle events can occur simultaneously within the same time region and it is crucial to accurately identify each particle track, including signals, to improve the robustness of track finding methods and enhance reconstruction efficiency. ¯p annihilation is one of the background events and produces multiple particles that can mimic signal events. Additionally, photons from radiative pion captures can produce a γ → $e+e−$ pair, which can be used to calibrate the Mu2e momentum scale and the resolution. The Mu2e track reconstruction sequence begins by grouping hits produced in the tracker based on time and z coordinate information, called TimeCluster, and selected hits are processed to reconstruct helices and determine their momentum. The current pattern recognition algorithms identify a single helix per TimeCluster for single track events. A new pattern recognition algorithm is being developed to reconstruct multi-particle events and its features for finding multiple tracks and the current evaluation results are reported.

Kitagawa, H. [Pisa U.]↗
DOE OSTI2025

Studies for the selection of fully contained muon neutrino CC events in the ICARUS T600 detector at Fermilab

The ICARUS T600 LAr-TPC detector has started its new physics runs in June 2022 at Fermilab within the SBN program. This detector is recording neutrino interactions from the Booster BNB neutrino beam in order to definitively clarify the open questions related to the possible existence of sterile neutrinos, as suggested by numerous observed experimental anomalies. In addition the neutrinos events recorded from the NuMI off-axis beam are under study in order to perform neutrino-Argon cross section measurements. At Fermilab, ICARUS is facing a challenging experimental condition: the detector is presently installed essentially at Earth surface where cosmic ray particles can become a serious source of background for the neutrino event search. This condition makes it necessary to deploy suitable automatic tools for the identification, selection, and measurement of the neutrino events among the millions of events triggered by cosmics. In this contribution, two automatic selection procedures devoted to the identification of fully contained muon neutrino interactions in the BNB neutrino beam will be presented, together with the first results of their application on simulated and on recently recorded events in the T600 detector.

Farnese, Christian [INFN, Padua] (ORCID:0000000163↗
DOE OSTI2025

Studies for the Selection of Fully Contained Muon Neutrino CC Events in the ICARUS T600 Detector at Fermilab

The ICARUS T600 LAr-TPC detector has started its new physics runs in June 2022 at Fermilab within the SBN program. This detector is recording neutrino interactions from the Booster BNB neutrino beam in order to definitively clarify the open questions related to the possible existence of sterile neutrinos, as suggested by numerous observed experimental anomalies. In addition the neutrinos events recorded from the NuMI off-axis beam are under study in order to perform neutrino-Argon cross section measurements. At Fermilab, ICARUS is facing a challenging experimental condition: the detector is presently installed essentially at Earth surface where cosmic ray particles can become a serious source of background for the neutrino event search. This condition makes it necessary to deploy suitable automatic tools for the identification, selection, and measurement of the neutrino events among the millions of events triggered by cosmics. In this contribution, two automatic selection procedures devoted to the identification of fully contained muon neutrino interactions in the BNB neutrino beam will be presented, together with the first results of their application on simulated and on recently recorded events in the T600 detector.

Farnese, Christian [INFN, Padua]↗
DOE OSTI2026

Search for NC Delta Radiative Decay Single Photon Events In MicroBooNE

MicroBooNE, a liquid argon time projection chamber at Fermilab, is investigating the MiniBooNE anomaly, which consists of an excess of low-energy electromagnetic showers in a neutrino beam. Recent results from MicroBooNE have ruled out a 3+1 sterile neutrino explanation of the anomaly, leaving the single photon-like explanation as the most likely possibility. In this poster, we describe results of a new expanded search for the neutral current Delta radiative decay topology, a rare type of event which is by far the largest expected source of neutrino-induced single photons. Using two different reconstruction paradigms simultaneously, we study in detail events both with and without visible hadronic activity; either category of event could explain the MiniBooNE anomaly, but the two could have very different implications for underlying physics. We find that events with visible protons are excluded as an explanation of the MiniBooNE anomaly, but events without visible protons remain a possible explanation requiring further study.

Hagaman, Lee [Nevis Labs, Columbia U.] (ORCID:0000↗
DOE OSTI2026

White-Rabbit-Disciplined FPGA Readout for Fermilab Timing Events

Fermilab's accelerator timing links broadcast short event codes to thousands of devices at once, but the links themselves carry no absolute notion of time; that comes separately from a White Rabbit reference. This work builds the piece that ties the two together on a single board. On a Xilinx Kria KR260 (Zynq UltraScale+), the programmable logic decodes a real Fermilab TCLK link, stamps every event with an absolute White-Rabbit \{sec, ns\} UTC time, and reads the timestamped stream out over AXI4-Lite; a thin Linux process on the same die publishes each event into a Redis stream on the control network. To exercise the full chain on one board, the decoded events are re-encoded as gigabit ACLK, transmitted out an SFP+ optical port, looped back over a short fiber jumper, and decoded again on the same timeline, and are additionally mirrored as an ACLK-Lite Manchester waveform for benchtop probing. Across sustained, multi-day testing against real Fermilab TCLK, the pipeline has decoded, timestamped, and published hundreds of millions of events with practically zero loss, and folding the timestamped stream on the 60-second accelerator supercycle recovers the machine's periodic structure directly from the published data.

Rossel, Jacob [Fermilab; UC, Berkeley (main)] (ORC↗
DOE OSTI2026

An Iterative Machine Learning Framework for Event Classification and Monte Carlo Tuning in SpinQuest

The E1039/SpinQuest experiment at Fermi National Accelerator Laboratory uses a 120~GeV proton beam from the Main Injector incident on transversely polarized proton and deuteron targets, using $NH_3$ and $ND_3$, respectively. In addition to measuring the Sivers asymmetry in Drell--Yan $pp$ and $pd$ scattering from sea quarks, SpinQuest will study transverse-spin effects, particularly the transverse single-spin asymmetry (TSSA) in $J/\psi$ production. The angular distributions from the $J/\psi$ decay could play an important role in understanding the gluon contribution to the proton spin structure. However, before extracting these angular distributions, it is necessary to isolate signal events originating from the target from events produced by other sources and from the combinatorial background. To effectively and accurately classify the target events, it is important to ensure that the simulated events are properly tuned to the experimental physics channels. We have introduced an iterative technique to match simulated and experimental events and to classify the physics channels using deep neural networks and a generative model based on normalizing flows.

Hossain, Forhad [Virginia U. (main)] (ORCID:000000↗
DOE OSTI2026

Observed Atmospheric River Events

This dataset contains observed landfalling atmospheric river (AR) events over the Northeast Pacific and U.S. West Coast used to initialize and evaluate the Deep-AR synthetic atmospheric river framework. It includes 1,434 held-out events whose observed records begin 48 hours before diagnosed AR landfall and continue at 6-hour intervals over a 144-hour event window. Each HDF5 file provides high-resolution 0.25° gridded fields of integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation. IVT components and precipitation are sourced from EDARA; 10 m winds are sourced from ERA5. The records share a common 200 × 480 grid over the Northeast Pacific and U.S. West Coast and include coordinate and datetime arrays. These events support atmospheric-river hazard analysis, evaluation of synthetic event generation, precipitation-extremes research, and regional stress testing.

17 WIND ENERGY↗
DOE OSTI2026

Persistent Elevated Soot Emissions Induced by Clustered Stochastic Preignition Events

Stochastic Preignition (SPI) is an abnormal combustion phenomenon that can occur in spark-ignition engines particularly under high-load operation. SPI is characterized by uncontrolled initiation of combustion prior to spark discharge, an abnormal combustion process that can lead to severe knock events and significant engine damage. SPI has been associated with fuel properties, lubricant composition, and engine design and operation. Here, in this work, a single-cylinder test engine with a dry-sump oil system was utilized to study the SPI response of E10 and E25 fuels with a range of Reid Vapor Pressure (RVP). An automated test procedure was employed, consisting of ten square-waved load profile segments, with each segment composed of 5 min of low-load operation followed by 25 min of sustained high-load operation. These tests were replicated across multiple days of testing including a lubricant triple flush between tests, and an online Fuel in Oil diagnostic measurement. Exhaust particulate emissions were continuously measured by an AVL microsoot sensor (MSS). Elevated particulate matter emissions were observed to occur concurrently with SPI events as blooms of soot. Particularly after clustered events (i.e., multiple SPI cycles occurring within 10 consecutive engine cycles), high soot emissions were observed to persist over several days of sequential operation despite daily lubricant changes, a complete warm-up procedure, and sustained low-load operation between test segments. This result implies that the particulate emissions trends may be dominated by deposit-based effects, where higher load operation is needed to alter deposition and formation processes. The observed soot blooms were also found to correspond to a reduction in the engine fueling and the fuel engine oil dilution rate despite the engine exhaust remaining at stoichiometric exhaust operation. These observations suggest that post-SPI events, pathways for lubricant migration and consumption into the combustion chamber may occur until these pathways are closed from deposit formation or ring dynamics during extended operation. These observed sooting propensity persisted with all fuels tests, but a linear correlation was observed between the summation of soot and particulate matter index (PMI) value for each fuel as well as SPI events, proving that PMI is a crucial fuel property for reducing SPI.

Splitter, Derek [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing ICARUS and REDTOP Software and Hardware: Event Generator Interface Development and Calorimeter Tile Prototype

ICARUS (Imaging Cosmic And Rare Underground Signals) is a liquid argon time projection chamber (LArTPC) detector that pursues the sterile neutrino, which relies on accurate simulations of neutrino-argon interactions. REDTOP (Rare Eta Decays To Observe new Physics) is a proposed low-energy, high-intensity meson factory designed to explore rare $\eta$/$\eta'$ meson decays and probe physics beyond the Standard Model. As a next-generation experiment, this requires both accurate simulations and innovative detector technologies. This project contributes to both ICARUS, from a simulation perspective, and REDTOP, from both a simulation and detection perspective, through the event generation of lepton-nucleon interactions and the physical enhancement of the calorimeter technology within the REDTOP detector. We developed an interface between ACHILLES (A CHIcago Land Lepton Event Simulator), a theory-driven lepton-level event generator, and GENIE, a robust event generator framework used for neutrino physics. By incorporating the precise theoretical cross-section calculations of ACHILLES into the experimental realism of GENIE, the interface allows for improved accuracy of neutrino-nucleon simulations, which can be adapted for the proton beam specifications of the REDTOP meson factory as well as for the ICARUS experiment. In parallel, we developed an improved prototype for the ADRIANO2 (A Dual Readout Integrally Active Non-segmented Option) dual-readout calorimeter tiles for the REDTOP detector. To improve the efficiency of the lead-glass tiles trapping Cherenkov light for energy reconstruction and particle identification, we optimized the application of a highly reflective coating. Through viscosity and thickness control, masking, and a custom spray technique, we refined the coating process to reduce surface defects and improve light yield. Together, these efforts strengthen the ICARUS neutrino program and REDTOP's capability of detecting rare decay events.

Visser, Erin [Michigan State U.] (ORCID:0009000184↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
DOE OSTI2024

WHOLESCALE: Microseismic Event Catalog for San Emidio, Nevada 2022

This submission includes a high-precision seismic event catalog estimated from seismic data collected at San Emidio, Nevada from April to May 2022. The catalog lists the precise time, location, and magnitude of microseismic events recorded during this period. Both the seismic data and microseismic event catalog were produced as part of the Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses (WHOLESCALE) project. Attached here are the microseismic event catalog, a link to the raw seismic data, and a detailed description of methods used to create the catalog.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

A sequential Attacker-Defender game for distribution systems resilience enhancement against extreme weather events

Improving distribution system resilience against frequent extreme weather events is important for reliable power system operations. Especially when dealing with events such as hurricanes that have short-term predictions, proactive pre-event preparedness plays a vital role in system resilience performance. In this paper, we propose a novel approach to construct pre-event resource allocation plans for system operators to cope with upcoming threats through a sequential attacker-defender game framework. The sequential attacker-defender game is designed to model the interaction between the extreme weather and the system operator. In each round of the game, the attacker and the defender sequentially update their current strategies by accounting for the opponent’s action set. The attacker model is formulated as a bi-level problem to identify the severe outage scenarios, and the defender model is formulated as a two-stage optimization problem to determine the allocation of restoration resources including mobile responsive resources and repair crews. Two scale-reduction strategies are proposed to ensure the scalability of the game scheme. Finally, case studies on the IEEE 33-bus and a 7149-node practical utility system validate the effectiveness of the proposed sequential game and the efficiency of the scale-reduction strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Trends and meteorological drivers of extreme daily reservoir evaporation events in the western United States

Extreme daily evaporation from reservoir surfaces can lead to significant short-term water losses, affecting water quality, water supply, and reservoir operation strategies. Historical trends in daily reservoir evaporation events have eluded the scientific and operational communities, largely due to a lack of long-term, consistent data record. This study quantifies trends in extreme daily reservoir evaporation events at 165 major reservoirs located in the western U.S. Here, we use the place-based energy balance and aerodynamic Daily Lake Evaporation Model (DLEM) driven by multiple meteorological data products (RTMA, gridMET, Daymet) to estimate daily evaporation rates at these reservoirs from 1981 to 2022. The results—while are based on different meteorological forcing datasets—consistently indicate that the California, Lower Colorado, and Rio Grande hydrologic regions are more prone to higher daily evaporation extremes. Compared to the rest of western U.S, these three regions also experience a more pronounced increasing trend in the annual maximum daily evaporation rate, at about 0.3 mm day -1 decade -1 during 1981-2022. The results show that heat and dryness are the main drivers to the increasing trend of extreme evaporation, while extreme wind speed is the primary contributor to exceptionally high daily evaporation events across all regions. This phenomenon is particularly prominent in the arid Lower Colorado region, but less significant in the cold and humid Pacific Northwest region. We also find that the correlation between extreme wind speed and extreme evaporation degrades as the time scale increases from daily, to monthly and seasonal. Our findings have strong implications for the pattern and distribution of extreme evaporation events at the western U.S. reservoirs, and illustrate how various drivers influence extreme evaporation across different time scales.

13 HYDRO ENERGY↗
DOE OSTI2024

More high-impact atmospheric river-induced extreme precipitation events under warming in a high-resolution model

Extreme precipitation events, as occurred in Europe 2021, or western North America 2023, with an intensity of 75 mm/day or 100 mm/day, respectively, exert a catastrophic impact. Lower-resolution (~100 km) climate models cannot simulate the intensity, nor their response to greenhouse warming. Using a high-resolution (~25 km) model capable of simulating such events, here we show that frequency-weighted area of such events over western Europe and the west coast of North America likely expands by more than 80% under an approximately 4°C of global warming from the historical levels. Along the west coasts of Europe and North America, area impacted by atmospheric rivers-induced extreme precipitation events is projected to double, driven by intensified landfalling atmospheric rivers. Thermodynamic processes drive the increase, whereas dynamic processes reduce the intensity over western Europe but enhance it along the west coast of North America. Here our findings provide policy-relevant information for climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗