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DOE OSTI2025

A Convolution Neural Network for Voltage Event Classification at a Photovoltaic Inverter

This paper presents a convolutional neural network (CNN) developed to identify voltage events in photovoltaic (PV) inverters. The CNN is trained on synthetic data generated using the IEEE 13-bus distribution feeder model and evaluated on field measured data collected from Energy Northwest’s Horn Rapids Solar, Storage, and Training (HRSST) facility. The study focuses on two common voltage events: faults and voltage sags. The CNN is configured to analyze voltage and current waveforms from three-phase PV systems, demonstrating excellent accuracy during training. Field data from the HRSST facility is employed to assess its real-world performance, where the CNN achieves perfect identification of faults and voltage sags in a sample of nine events. This work highlights the potential of the proposed method to enhance PV protection schemes, providing a robust foundation for improved voltage event detection and grid reliability.

Cornachione, Matthew A.↗
DOE OSTI2026

Protection System Validation Using Post-Event Anomaly Classification with Machine Learning

Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Multi‐Sensor Trajectory Reconstruction of the 24 April 2025 Alaska Fireball and Implications for Planetary Defense

On 24 April 2025 at 18:30:57 UTC, a bright daytime fireball over Southcentral Alaska was detected by 37 seismic stations, 16 single infrasound sensors, and four infrasound arrays, yielding 30 ballistic and multiple fragmentation arrivals. Here, the unprecedented density of seismoacoustic coverage enabled detailed reconstruction of the event using acoustic signals, with fragmentation source locations further guiding the identification of Doppler weather radar signatures of a meteorite fall. Incorporation of a radar-derived terminal point yielded a final trajectory solution, which agreed closely with an independent optical trajectory solution from video analysis. The reconstructed entry parameters from seismoacoustic analysis indicate a velocity of 25.3 km/s, an entry angle of 19°, and an energy release of ∼38 t TNT equivalent. Assuming a chondritic composition, the pre-entry object diameter was ∼0.7 m. Using orbital parameters from the optical solution, we estimate meteoroid composition as most likely an L-type ordinary chondrite. The event occurred in the sub-Arctic, where space-based optical systems face challenges in detection, demonstrating the critical role of dense ground-based seismoacoustic networks in characterizing high-latitude atmospheric entries. This uniquely well-recorded event demonstrates the capability of dense seismoacoustic networks to constrain bolide trajectories, energetics, and fragmentation, with radar and optical data providing critical confirmation and complementary perspectives. These results bridge the methodological gap between planetary-defense monitoring of natural impactors and space-traffic analyses of artificial reentries, illustrating how multi-sensor integration can deliver calibration-grade trajectories even for unpredicted events.

Fireball↗
DOE OSTI2026

Characterization of Precipitation-Induced Radon Progeny Deposition Events Using a City-Scale Sensor Network

Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. Here, we present a quantitative and qualitative description of rain-induced radon progeny deposition events occurring in Chicago from September 2023 to February 2024. We measure ambient gamma radiation levels, precipitation rate, temperature, pressure, and relative humidity in a network of sensor nodes. For each identified precipitation period, we decompose spectra into static- and radon-associated components as defined by a non-negative matrix factorization (NMF) algorithm. We find a consistent power-law relationship between a precipitation-dependent peak of the radon progeny proxy (RPP) and the peak strength of the radon-associated NMF component for most precipitation events. We conduct a case study of a rainfall period with abnormally high levels of implied radon progeny concentration and describe its temporal and spatial evolution. We hypothesize that this phenomenon is due to the air mass path that intersects a uranium-rich region of Wyoming. Finally, we cluster precipitation events into three distinct categories. One category roughly corresponds to events with deep low-pressure systems and high relative radon concentration, while another is characteristic of light stratiform rain with slightly higher temperatures and intermediate relative radon concentration. The third category appears to contain weak-gradient or lake breeze convection showers with intermittent precipitation and low relative radon concentration. These findings suggest that radiological anomaly detection could be improved by training unique background models corresponding to each category of meteorological event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Single Photon Searches at ICARUS with SPINE

The MiniBooNE Low-Energy Excess (LEE) of electron-like events from the Booster Neutrino Beam (BNB) has puzzled neutrino physicists for decades. One possible explanation has been an unpredicted excess of neutral current (NC) $\Delta$ resonance interactions with a subsequent radiative decay. An increase in the rate of NC $\Delta\rightarrow N\gamma$ events by a factor of 3.18 could explain the LEE seen by MiniBooNE. ICARUS also sees neutrinos from the BNB and can check the rate of these single photon events. The SPINE particle physics reconstruction suite leverages deep neural networks (DNNs) to optimize particle reconstruction and identification in Liquid Argon Time Projection Chambers (LArTPCs). I present preliminary findings on the effectiveness of these machine learnign (ML) techniques for single photon event reconstruction at ICARUS.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2024

Fireball antinucleosynthesis

The tentative identification of approximately ten relativistic antihelium ( He ¯ ) cosmic-ray events at AMS-02 would, if confirmed, challenge our understanding of the astrophysical synthesis of heavy antinuclei. We propose a novel scenario for the enhanced production of such antinuclei that is triggered by isolated, catastrophic injections of large quantities of energetic Standard Model (SM) antiquarks in our galaxy by physics beyond the Standard Model (BSM). We demonstrate that SM antinucleosynthetic processes that occur in the resulting rapidly expanding, thermalized fireballs of SM plasma can, for a reasonable range of parameters, produce the reported tentative ∼ 2 : 1 ratio of He ¯ 3 to He ¯ 4 events at AMS-02, as well as their relativistic boosts. Moreover, we show that this can be achieved without violating antideuterium or antiproton flux constraints for the appropriate antihelium fluxes. A plausible BSM paradigm for the catastrophic injections is the collision of macroscopic composite dark-matter objects carrying large net antibaryon number. Such a scenario would require these objects to be cosmologically stable, but to destabilize upon collision, promptly releasing a fraction of their mass energy into SM antiparticles within a tiny volume. We show that, in principle, the injection rate needed to attain the necessary antihelium fluxes and the energetic conditions required to seed the fireballs appear possible to obtain in such a paradigm. We leave open the question of constructing a BSM particle physics model to realize this, but we suggest two concrete scenarios as promising targets for further investigation. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Fermi -LAT follow-up observations in seven years of real-time high-energy neutrino alerts

The realtime program for high-energy neutrino track events detected by the IceCube South Pole Neutrino Observatory releases alerts to the astronomical community with the goal of identifying electromagnetic counterparts to astrophysical neutrinos. Gamma-ray observations from theFermi-Large Area Telescope (LAT) enabled the identification of the flaring gamma-ray blazar TXS 0506+056 as a likely counterpart to the neutrino event IC-170922A. By continuously monitoring the gamma-ray sky,Fermi-LAT plays a key role in the identification of candidate counterparts to realtime neutrino alerts. In this paper, we present theFermi-LAT strategy for following up high-energy neutrino alerts applied to seven years of IceCube data. Right after receiving an alert, a search is performed in order to identify gamma-ray activity from known and newly detected sources that are positionally consistent with the neutrino localization. In this work, we study the population of blazars found in coincidence with high-energy neutrinos and compare them to the full population of gamma-ray blazars detected byFermi-LAT. We also evaluate the relationship between the neutrino and gamma-ray luminosities, finding different trends between the two blazar classes BL Lacs and flat-spectrum radio quasars.

Astronomy & Astrophysics↗
DOE OSTI2025

Detection of multi-modal Doppler spectra – Part 1: Establishing characteristic signals in radar moment data

Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.

Wugofski, Sarah [Pennsylvania State Univ., Univers↗
DOE OSTI2024

Quantifying the economic costs of power outages owing to extreme events: A systematic review

Quantifying the economic cost of long-duration power outages is crucial to justifying investments in resiliency and reliability improvements. However, extensive study on the subject complicates the identification of power outage costs and determining the most suitable approach to quantify them for an individual, specific facility, particularly in the context of extreme events. Here, this research provides a systematic review of economic studies estimating the impact of environmental disasters at the microeconomic and macroeconomic levels. Of 326 articles, evaluating the costs of power outages in extreme events, this work identified 22 studies that attempted to quantify the economic costs. These findings indicate that quantifying power outage costs lacks standardization, posing challenges for comparing different studies. Most analyses aiming to quantify these costs for utilities, sectors, and the overall economy rely on outdated survey data, which offer generalized rather than specific cost estimations. The costs of power outages exhibit a significant dependence on factors such as the sector involved, the type of customer affected, and the outage duration. To quantify industry costs, the research in this study suggests that using the National Renewable Energy Laboratory's online, open-access Customer Damage Function Calculator is the best option for individual-level assessments of industries, hospitals, offices, education centers, and similar facilities. However, the Interruption Cost Estimate Calculator can estimate outage costs across industrial, commercial, and residential sectors for macroeconomic outcomes. Finally, this article discusses the relative strengths of these methods and tools and the potential directions for future research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗
DOE OSTI2024

Preliminary Screening of Features, Events, and Processes for an Arctic-Focused Climate Intervention Performance Assessment

Geoengineering, the deliberate large-scale intervention in Earth's climate system, holds significant potential in the rapidly warming Arctic, where temperatures currently rise at more than twice the global average, accelerating ice sheet and permafrost melt. This contributes to global sea-level rise and releases methane, a potent greenhouse gas. Strategies like solar radiation management (SRM) and carbon dioxide removal (CDR) could mitigate these effects; for instance, SRM techniques aim to reflect a portion of the sun's energy back into space, potentially slowing ice melt and stabilizing permafrost. However, geoengineering in the Arctic faces challenges, including potential unintended consequences on the fragile ecosystem, disruption of local weather patterns, and impacts on indigenous communities. Effective governance requires robust international cooperation, environmental impact assessments, and regulatory frameworks. Despite these challenges, geoengineering's potential benefits make it a critical research area. This report explores application of the Performance Assessment (PA) methodology to Arctic Climate Intervention, providing an initial screening of relevant features, events, and processes (FEPs). At the core of the PA approach is the identification and evaluation of FEPs that could impact the performance of the intervention scheme. Here we provide an initial screening of FEPs to consider in the application of PA to Arctic Climate Intervention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Expanding the physics reach of DUNE in the near and far detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment. Its primary goal is the determination of the neutrino mass hierarchy and the CP-violating phase. The DUNE physics programme also includes the detection of astrophysical neutrinos and the search for beyond the Standard Model (BSM) phenomena. DUNE will consist of a near detector (ND) complex placed at Fermilab, and a modular Liquid Argon Time Projection Chamber (LArTPC) far detector (FD) to be built in the Sanford Underground Research Facility (SURF), approximately 1300 km away from the neutrino production point. This thesis describes three different projects within DUNE. First, a novel strategy to improve the triggering capabilities of the DUNE FD is proposed. It uses matched filters to enhance the production of online hits across all charge collection planes. Next, the possibility of detecting neutrinos coming from dark matter (DM) annihilations in the Sun with the FD is explored. The complementarity of DUNE to this kind of DM searches is shown. Finally, the simulation and reconstruction framework of ND-GAr, the gas argon ND proposed for Phase II of DUNE, is presented. A number of additions to this are described, particularly focused on the development of the particle identification (PID) capabilities of the detector. These are then used to perform the first event selection studies with an end-to-end simulation in ND-GAr, in particular the selection of pion exclusive samples in $\nu_{\mu}$ CC interactions. All three of these projects share the common goal of enhancing the physics programme of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

MEASURING CLAS12 D(E, E′Π±) CROSS SECTIONS FOR E4NU

Neutrino experiments need neutrino event generators such as GENIE to simulate neutrinonucleus (¿A) interactions in order to measure neutrino oscillations. We need eA data to validate GENIE. GENIE d(e, e') cross sections do not match data in the pion production region. Further analysis of this region can help constrain GENIE models. The goal of this project was to compare 4.244 GeV CLAS12 d(e, e'p±) cross sections to GENIE predictions. We analyzed data from the Fall 2019 run period of Run Group B (RG-B). We applied particle identification, fiducial, and vertex cuts on electron and charged pion candidates. We compared the measured data with events generated with GENIE and another generator called onepigen. We used onepigen to simulate single charged pion production and to calculate radiative corrections for the data. We submitted GENIE and onepigen events to the GEant4 Monte-Carlo (GEMC) simulation of CLAS12 and applied the same cuts we used on the data. We plotted cross sections as functions of W and binned the events in Q2, ¿pq, and Pp. We used 2D (Q2), 3D (Q2 with ¿pq or Pp), and 4D (Q2, ¿pq, and Pp) binning schemes. We found GENIE describes d(e, e'p±) cross sections better than expected. GENIE describes the data remarkably well in the 2D bins and some 3D and 4D bins. There are many discrepancies between GENIE and data in the other 3D and 4D bins. The results show that, relative to data, GENIE cross sections increase as Q2 increases, decrease as Pp increases, and fit best at low ¿pq. These results will help guide improvements to GENIE in order to reduce the systematic uncertainties in neutrino-oscillation experiments.

Fogler, Caleb [Old Dominion Univ., Norfolk, VA (Un↗
DOE OSTI2026

Validating Protection System Behavior with Machine Learning in a Master State Overseer

As power system protection devices continue the widespread transition from analog to digital, they become increasingly intricate. The internal functions and communication between critical grid components must now be significantly more complex to keep up with the demands of the modern smart grid. This brings increased difficulty in maintenance and monitoring, making it harder to identify potential misoperation, power anomalies, and cyber threats. Such issues are often only pinpointed after an exhaustive and costly post-mortem analysis, when a major outage or damage has already occurred. A solution is needed for validating protection systems as they operate, independently evaluating grid state and confirming whether the protection system is behaving accordingly. As opposed to incident response, this acts as a constant verification mechanism that raises a flag when subtler issues are noticed, catching them earlier and preventing larger incidents. This work presents the implementation of such a system, expanding on the prototype developed by the authors in a previous paper. This is accomplished with a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. Additionally, this system is contextualized within a larger, modular Master State awareness Overseer (MSO) framework, responsible for monitoring, analyzing, and managing an electric grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Event Reconstruction for Water-based Liquid Scintillator Detectors

Water Cherenkov and liquid scintillator detectors represent two complementary approaches in neutrino physics. Water Cherenkov detectors provide particle track direction and particle identification (PID) through Cherenkov ring topology, while liquid scintillator detectors offer higher light yield and lower energy thresholds. Water-based liquid scintillator (WbLS) is designed to combine the advantages of both technologies. However, the simultaneous detection of Cherenkov and scintillation light by photosensors introduces significant challenges for event reconstruction. This poster presents event reconstruction studies for WbLS detectors. A likelihood-based reconstruction framework, fiTQun, has been successfully used in Super-Kamiokande to reconstruct water Cherenkov events in cylindrical detectors. We extend and improve this framework to demonstrate event reconstruction in WbLS detectors, enabling the concurrent reconstruction of particle energy, PID, vertex, and direction. The results demonstrate competitive performance in energy resolution, PID separation, and vertex and direction reconstruction, highlighting the strong potential of WbLS for next-generation neutrino experiments.

Xie, Zhenxiong [Minnesota U.] (ORCID:0009000301442↗
DOE OSTI2025

A Twin Circuit Theory-Based Framework for Oscillation Event Analysis in Inverter-Dominated Power Systems With Case Study for Kaua‘i System

Here, this paper proposes a real-world oscillation event analysis framework for power systems that include inverter-based resources together with synchronous generators. Specifically, the proposed framework combines both measurement-and model-based techniques to readily identify potential oscillation sources, replay the oscillation event with numerical simulation, unveil the underlying oscillation mechanism, and suggest mitigation methods for a wide range of oscillation events. To strengthen the theoretical foundation of our analysis framework, this paper proposes a twin circuit theory that provides theoretical support for one key utilized but not well-proven measurement-based oscillation source identification method-Dissipating Energy Flow. Our twin circuit theory also shows that adopting well-tuned grid-forming inverters can be a potential mitigation method for oscillation events. Finally, the effectiveness of our proposed oscillation event analysis framework is demonstrated by addressing a real-world 18-20 Hz oscillation event in Kaua‘i's power system on November 21, 2021.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Neutron Tagging From Neutrino Interactions in DUNE-ND 2x2 Prototype

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment that aims to measure whether CP is violated in the leptonic sector (if violated, how much) and unambiguously determine the neutrino mass ordering. DUNE consists of near and far detectors that rely on liquid argon time projection chamber (LArTPC) technology to observe neutrino interactions. The near detector (ND) will be placed in Fermilab, near the neutrino source, while the far detector (FD) will be deployed in Sanford Lab, 1.5 km deep underground, which is 1300 km away from the source. LArTPCs provide excellent particle identification and calorimetry; however, detecting neutrons is challenging, as they do not leave direct ionization signals in LArTPCs. Neutrons can carry away up to 25% of the neutrino energy, introducing a significant uncertainty in DUNE measurements. The DUNE near detector (ND) features a novel modular LArTPC with pixelated charge readout, which enhances event recons truction. The modular design enables precise correlation between ionization signals and light signals in a high-rate environment, improving the identification of delayed energy depositions from neutrons in neutrino interactions. We introduce a neutron tagging technique using the 2x2 Demonstrator, a small-scale prototype of the DUNE ND LArTPC. The analysis utilizes Monte Carlo simulations and deep-learning techniques to identify neutron-induced energy depositions and reconstruct low-energy activity.

Kufatty, Georgette [Florida State U.]↗