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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗
DOE OSTI2026

Seismic Event Characterization Using Full Moment Tensors on the Hypersphere

Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.

Geosciences↗
DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗
DOE OSTI2025

Bayesian event categorization matrix approach for explosion monitoring

Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.

58 GEOSCIENCES↗
DOE OSTI2025

Achilles and GENIE Model Comparisons Between Quasi-Elastic Proton Exclusive Events in DUNE PRISM ND-LAr

In this study, we analyze simulated CCQE proton exclusive events in DUNE s ND-LAr using Achilles and GENIE Monte Carlo event generators. We generate events using muon neutrino exclusive flux files of DUNE s on-axis and 20m off-axis PRISM positions and compare various kinematics of proton exclusive events between the event generators. Events are further analyzed in categories according to the number of final-state protons. This study was motivated by QE s dominance in statistics for off-axis events over other channels in which off-axis events are very useful for characterizing DUNE s flux. Furthermore, our motivation for comparing results between generators is driven by off-axis QE neutrino spectra peaking below 1 GeV. In this region, many low-energy proton events are produced in which models have been shown to have disagreements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Mitigating cosmic-ray-like correlated events with a modular quantum processor

Quantum processors based on superconducting qubits are being scaled to larger qubit numbers, enabling the implementation of small-scale quantum error-correction codes. However, catastrophic chip-scale correlated errors have been observed in these processors, attributed to, e.g., cosmic ray impacts, which challenge conventional error-correction codes such as the surface code. These events are characterized by a temporary but pronounced suppression of the qubit-energy relaxation times. Here, in this study, we explore the potential for modular quantum computing architectures to mitigate such correlated energy decay events. We measure cosmic-ray-like events in a quantum processor comprising a motherboard and two flip-chip bonded daughterboard modules, each module containing two superconducting qubits. We monitor the appearance of correlated qubit decay events within a single module and across the physically separated modules. We find that while decay events within one module are strongly correlated (over 85%), events in separate modules only display approximately 2% correlations. We also report coincident decay events in the motherboard and in either of the two daughterboard modules, providing further insight into the nature of these decay events. These results suggest that modular architectures, combined with bespoke errorcorrection codes, offer a promising approach for protecting future quantum processors from chip-scale correlated errors.

Wu, Xuntao [Univ. of Chicago, IL (United States)] ↗
DOE OSTI2024

Understanding Event Trajectories Across Massive Temporal Datasets with Word Embeddings and Visualization

In collaboration with researchers from Virginia Tech, Savannah River National Laboratory has continued development of a natural language processing pipeline to identify and extract events of interest from massive open data sources in the domain of worldwide state-sponsored civil nuclear energy. The foundation of the pipeline is built on compass aligned temporal word embedding models, whereby contextual shifts are automatically identified by comparing keyword embedding vectors across successive time windows. Within the approach, a contextual shift indicates the occurrence of a potential event of interest. However, in such a broad topical domain that captures events at a global scale, across various life cycle stages, and across numerous different technology types, a user that is monitoring events may have broad interests in capturing many different event types with varying degrees of signal. As such, the quantity of information that may be returned from an automated event extraction pipeline can be substantial, requiring manual effort to sift through the information to identify any relevant bits of information. Therefore, a more streamlined workflow that aids in directing a user toward specific information at different points in time is necessary. The workflow presented here has been developed with this concept in mind, built on top of the initial prototype event extraction pipeline, whereby a user can analyze temporal text-based data sources at multiple different contextual levels to isolate key points in time and key subdomains captured within a data corpus. Using multiple corpuses that consist of approximately 7 million Tweets and 7 million news articles, the team has extended compass aligned temporal word embedding models to establish an interconnected and hierarchical structure that relates known key words of interest to documents, local topics (i.e., within a time window), and global topics across the corpuses. All of this information is packaged into a visual analytics system that is linked to the information extraction pipeline and enables a user to identify contextual information that describes the evolution of a high dimensional embedding space across time to isolate changes of interest and explore associated events. This report demonstrates the use of these analytics and a means to fuse information across multiple datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Radiation Imaging with Event Camera

Neuromorphic or event-based imaging is a new, commercially available sensor technology inspired by how the human eye works. Instead of measuring frames at a fixed rate, the camera measures changes in pixel intensity asynchronously. This difference in readout architecture results in a high dynamic range and low latency. Event-based cameras have been used in a variety of applications, including object tracking, navigation, and lidar technologies. However, event-based cameras have not been adequately researched for their ability to image high-energy particles. This report explores the use of an event camera for imaging alpha, beta, and X-rays particles, when coupled with scintillator screens to convert high-energy particles into visible light. Methods to process event data were developed and are presented here, along with the results. The event camera can measure alpha and beta particles with comparable performance to that of a conventional camera. Event cameras can also image higher-activity sources and offer the possibility of discriminating particle interaction types on the basis of timing differences, which typical cameras cannot do. Additionally, event cameras can image objects with an X-ray source when the source strength dynamically changes but does not create a high-contrast image during static X-ray measurement.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Object detection with deep learning for rare event search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Convolutional neural network↗
DOE OSTI2024

Sampling Rare Events in Aqueous Systems Using Molecular Simulations

Birth of a new distinct phase is a phenomenon encountered in a myriad of processes, and has wide ranging consequences in material processing, biological self-assembly, separations and several other processes. Several phase transitions are nucleation driven. The nucleation events occur over nanosecond timescales and involve hundreds to thousands of molecules. These length and timescales are difficult to access in experiments, thereby making experimental studies of nucleation challenging. On the other hand, molecular simulations sample the nanosecond and nanometer scales making them ideal to study nucleation. However, nucleation is a rare event, meaning that the waiting time to observe one nucleation event is significant. This makes simulation studies of rare events challenging. The project focused on a multi-pronged approach to address such challenges to develop the next generation rare event sampling methods for molecular simulations. The key outcomes of our work include developing more effective methods for sampling rare events, utilizing machine learning to better elucidate nucleation mechanisms, development of software for easy implementation of the methodologies, and applications of the methods to realistic systems to push the method applicability beyond model systems. Overall, this work has enabled pushing the frontiers of molecular simulations to study rare events with a focus on nucleation in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Classification of events from α -induced reactions in the MUSIC detector via statistical and ML methods

The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the MUSIC data produced in one experiment scientists carefully extract an order of 10 3 events of interest from about 10 9 total events, where each event can be represented by an 18-dimensional vector. However, the standard data classification process is based on expert driven, manually intensive data analysis techniques that require several months to identify patterns and classify the relevant events from the collected data. Here, to address this issue, we present a method for the classification of events originating from specific α-induced reactions by combining statistical and machine learning methods that require significantly less input from the domain scientist, relative to the standard technique. Here, we applied the new method to two experimental data sets and compared our results with those obtained using traditional methods. With few exceptions, the number of events classified by our method agrees within ±20% with the results obtained using traditional methods. With the present method, which is the first of its kind for the MUSIC data, we have established the foundation for the automated extraction of physical events of interest from experiments using the MUSIC detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Precursory Locking Precedes Slip Events on Laboratory Fault

Earthquakes occur through repeated cycles of locking and slip. What is the relationship between transiently locked patches and the size, location, and timing of the subsequent slip events? Here we use an experimental fault made of transparent rubber to image the slip history of over 18,000 events. The soft elastic system allows for contained ruptures and thus a natural evolution of stress heterogeneity over multiple earthquake cycles. We observe a precursory locking phase preceding slip nucleation for events of all sizes. The integrated locking distance increases linearly with the moment of the eventual event. The most intense (largest slip deficit) locking events are spatially and temporally correlated with the hypocenters of the largest rupture events. These results show that the transient locking is statistically, but not deterministically, predictive of the location, timing, and size of the eventual slip event.

earthquakes↗
DOE OSTI2024

Review of the August 1972 and March 1989 (Allen) Space Weather Events: Can We Learn Anything New From Them?

Abstract Updated summaries of the August 1972 and March 1989 space weather events have been constructed. The features of these two events are compared to the Carrington 1859 event and a few other major space weather events. It is concluded that solar active regions release energy in a variety of forms (X‐rays, EUV photons, visible light, coronal mass ejection (CME) plasmas and fields) and they in turn can produce other energetic effects (solar energetic particles (SEPs), magnetic storms) in a variety of ways. It is clear that there is no strong one‐to‐one relationship between these various energy sinks. The energy is often distributed differently from one space weather event to the next. Concerning SEPs accelerated at interplanetary CME (ICME) shocks, it is concluded that the Fermi mechanism associated with quasi‐parallel shocks is relatively weak and that the gradient drift mechanism (electric fields) at quasi‐perpendicular shocks will produce harder spectra and higher fluxes. If the 4 August 1972 intrinsic magnetic cloud condition (southward interplanetary magnetic field instead of northward) and the interplanetary Sun to 1 au conditions were different, a 4 August 1972 magnetic storm and magnetospheric dawn‐to‐dusk electric fields substantially larger than the Carrington event would have occurred. Under these special interplanetary conditions, a Miyake et al. (2012), https://doi.org/10.1038/nature11123 ‐like extreme SEP event may have been formed. The long duration complex 1989 storm was probably greater than the Carrington storm in the sense that the total ring current particle energy was larger.

Tsurutani, Bruce T.↗
DOE OSTI2025

Advances in geophysical forensic event monitoring

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. Here, in this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine.

geophysics↗
DOE OSTI2025

Search for the Anomalous Events Detected by ANITA Using the Pierre Auger Observatory

A dedicated search for upward-going air showers at zenith angles exceeding 110° and energies E > 0.1 EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two “anomalous” radio pulses observed by the ANITA flights I and III that appear inconsistent with the standard model of particle physics. Using simulations of both regular cosmic-ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1–33] EeV. One event has been found in the search period between January 1, 2004, and December 31, 2018, consistent with an expected background of 0.27 ± 0.12 events from misreconstructed cosmic-ray showers. This translates to an upper bound on the integral flux of ( 7.2 ± 0.2 ) × 10 − 21 cm − 2 sr − 1 y − 1 and ( 3.6 ± 0.2 ) × 10 − 20 cm − 2 sr − 1 y − 1 for an E − 1 and E − 2 spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an E − 3 spectrum and over 8.1 events for a conservative E − 5 spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers. Published by the American Physical Society 2025

Abdul Halim, A.↗
DOE OSTI2025

Optimization of X-ray event screening using ground and in-orbit data for the Resolve instrument onboard the XRISM satellite

The X-Ray Imaging and Spectroscopy Mission (XRISM) satellite was successfully launched and put into a low-Earth orbit on September 6, 2023 (UT). The Resolve instrument onboard XRISM hosts an X-ray microcalorimeter detector, which was designed to achieve a high-resolution ( ≤ 7 eV FWHM at 6 keV), high-throughput, and non-dispersive spectroscopy over a wide energy range. It also excels in a low background with a requirement of < 2 × 10 -3 s -1 keV -1 (0.3 to 12.0 keV), which is equivalent to only one background event per spectral bin per 100-ks exposure. Event screening to discriminate X-ray events from background is a key to meeting the requirement. We present the result of the Resolve event screening using data sets recorded on the ground and in orbit based on the heritage of the preceding X-ray microcalorimeter missions, in particular, the Soft X-ray Spectrometer onboard ASTRO-H. We optimize and evaluate 19 screening items of three types based on (1) the event pulse shape, (2) relative arrival times among multiple events, and (3) good time intervals. We show that the initial screening, which is applied for science data products in the performance verification phase, reduces the background rate to 1.8 × 10 -3 s -1 keV -1 meeting the requirement. We further evaluate the additional screening utilizing the correlation among some pulse shape properties of X-ray events and show that it further reduces the background rate, particularly in the < 2 keV band. Over 0.3 to 12 keV, the background rate becomes 1.0 × 10 -3 s -1 keV -1 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Machine learning–based extreme event attribution

The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning–based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Demonstration of event position reconstruction based on diffusion in the NEXT-white detector

Noble element time projection chambers are a leading technology for rare event detection in physics, such as for dark matter and neutrinoless double beta decay searches. Time projection chambers typically assign event position in the drift direction using the relative timing of prompt scintillation and delayed charge collection signals, allowing for reconstruction of an absolute position in the drift direction. In this paper, alternate methods for assigning event drift dis tance via quantification of electron diffusion in a pure high pressure xenon gas time projection chamber are explored. Data from the NEXT-White detector demonstrate the ability to achieve good position assignment accuracy for both high and low-energy events. Using point-like energy deposits from 83m Kr calibration electron captures (E ~ 45 keV), the position of origin of low-energy events is determined to 2 cm precision with bias < 1 mm. A convolutional neural network approach is then used to quantify diffusion for longer tracks (E ≥ 1.5 MeV), from radiogenic electrons, yielding a precision of 3 cm on the event barycenter. The precision achieved with these methods indicates the feasibility energy calibrations of better than 1% FWHM at Q ββ in pure xenon, as well as the potential for event fiducialization in large future detectors using an alternate method that does not rely on primary scintillation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗