Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Event detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Natural ≳100× Telescope: Discovery of the Strongly Lensed Type II SN 2025mkn at z = 1.37

We present the discovery of SN 2025mkn, a gravitationally lensed Type II supernova. First detected as a blue transient in Zwicky Transient Facility (ZTF), 0. "83 from a z = 0.42 elliptical galaxy, the follow-up SNIFS/UH2.2 m and LRIS/Keck spectra revealed absorption lines at z = 1.371. Later JWST NIRCam imaging shows that the bright transient is a close pair of point sources separated by 0. "07, and a 30 times fainter counterimage opposite the lens, for which NIRSpec reveals strong Hα emission also at z = 1.371. The lightcurves and spectra are consistent with the Type II supernova source being magnified ≳100 times, with ∼250 required to reconcile its luminosity with that of nearby events such as SN 2023ixf. Lens models are consistent with such high magnifications, and always show that the faint image arrived first (undetected in earlier ZTF imaging), consistent with the later spectral phase of this fainter image. A fourth image is also predicted and possibly detected in the NIRSpec data. Lightcurve-based time-delay measurements are not possible due to the first image being the faintest; however, the resolved NIRSpec spectra offer a future opportunity for time-delay cosmography through supernova phase measurements.

Strong gravitational lensing↗
DOE OSTI2020

Discriminating Uranium Isotopes Based on Fission Signatures Induced by Delayed Neutrons

The use of active interrogation to induce delayed neutron emission is a well-established technique for the characterization of special nuclear materials. Delayed neutrons have isotope-characteristic spectral and temporal signatures, which provide the basis for isotope identification. However, in bulk materials that contain an appreciable fissile (e.g., 235 U or 233 U) fraction, such as highly enriched uranium (HEU), delayed neutrons have a high probability of inducing additional fissions. As a result, the overall delayed neutron signature consists of two distinct components: the “primary” delayed neutrons (emitted directly by fission fragments) and the “secondary prompt” fission neutrons produced in fission induced by primary delayed neutrons. These prompt products differ from “primary” delayed neutrons both in their energy spectra and in the presence of coincident radiation released by the parent fission event. The presence and relative quantity of prompt products from delayed fission depend on the cross section of the material in the energy range of delayed neutrons, which may differ significantly between isotopes, thus providing an exploitable means for isotope differentiation. Here, we demonstrate two experimental approaches for discriminating between 235 U and 238 U isotopes based on the measurement of delayed neutron-induced fission products. First, HEU and depleted uranium objects are differentiated through the detection of high-energy prompt neutrons from delayed fission using both recoil-based organic liquid scintillators and thermalization spectra from a custom-built capture-gated composite detector. Secondly, coincident radiation measurements are used as the basis for discrimination by comparing the overall rates and time evolution of fission events when delayed neutrons are present.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Gamma-ray imaging of Np-237 metal using an organic glass imager

Neutron and gamma-ray imaging systems are deployed within the field of nuclear safeguards for the detection and localization of special nuclear materials and other materials of interest. 237 Np is one of these materials of interest due its presence in spent nuclear fuel and potential for use in nuclear weapons when purified. Here, for the first time, a 6 kg neptunium sphere (98.8 wt% 237 Np) was measured using a dual-particle imager, from the University of Michigan, consisting of organic glass and inorganic scintillators. The novel composition of organic glass scintillator was recently developed at Sandia National Labs and has been used in particle imaging systems due to its time resolution and particle discrimination capabilities. Gamma-ray energy spectra from single and coincident events were extracted and the sequencing of Compton scatter and photoelectric absorption gamma-ray events was used to generate images using simple backprojection. The emissions of interest in this work are the 312 keV and 416 keV gamma rays from 233 Pa, a daughter isotope from the neptunium decay series. The results of this work show that there is close agreement between the true source location in angular space and the converged location from the gamma ray images created using the system. The gamma spectroscopy from single and coincident events also identified the characteristic emission from the daughter isotope and could be used to assist with the identification of 237 Np. Furthermore, successful localization of the source with 5 s of data demonstrates the practical application of the imaging system for imaging and detection of material in weapons-useable quantities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0° (nadir) and 50°. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Detection of Grid-Signal Distortions Using the Spectral Correlation Function

This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal distortions. Our approach differs from existing treatments of signal distortion in its analysis of the varied spectral content of signals observed in real-world scenarios. The method we propose has state-of-the-art discriminative power that provides meaningful and understandable characterizations of various grid events and anomalies. In conclusion, to validate the approach, we use real world data from the Grid Event Signature Library, which is maintained jointly by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2021

Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport that are responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares nine global AR detection algorithms from the perspective of AR lifecycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980–2017. Uncertainties in AR lifecycle characteristics, including event number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum AR origin (termination) frequency is located over the western (eastern) portion of ocean basins, significant disagreements appear in regional distribution. Spreads are large in AR lifetime and intensity. Here, the number of landfalling AR events produced by the algorithms can vary from 16 to 80 events per year, although the agreement improves for stronger ARs. By examining the ARs' connections with the Madden-Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfall activity) to climate variability are consistent among algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fusion and fission events regulate endosome maturation and viral escape

Endosomes are intracellular vesicles that mediate the communication of the cell with its extracellular environment. They are an essential part of the cell’s machinery regulating intracellular trafficking via the endocytic pathway. Many viruses, which in order to replicate require a host cell, attach themselves to the cellular membrane; an event which usually initiates uptake of a viral particle through the endocytic pathway. In this way viruses hijack endosomes for their journey towards intracellular sites of replication and avoid degradation without host detection by escaping the endosomal compartment. Recent experimental techniques have defined the role of endosomal maturation in the ability of enveloped viruses to release their genetic material into the cytoplasm. Endosome maturation depends on a family of small hydrolase enzymes (or GTPases) called Rab proteins, arranged on the cytoplasmic surface of its membrane. Here, we model endosomes as intracellular compartments described by two variables (its levels of active Rab5 and Rab7 proteins) and which can undergo coagulation (or fusion) and fragmentation (or fission). The key element in our approach is the “per-cell endosomal distribution” and its dynamical (Boltzmann) equation. The Boltzmann equation allows us to derive the dynamics of the total number of endosomes in a cell, as well as the mean and the standard deviation of its active Rab5 and Rab7 levels. We compare our mathematical results with experiments of Dengue viral escape from endosomes. The relationship between endosomal active Rab levels and pH suggests a mechanism that can account for the observed variability in viral escape times, which in turn regulate the viability of a viral intracellular infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

TAROGE-M: radio antenna array on antarctic high mountain for detecting near-horizontal ultra-high energy air showers

The TAROGE-M radio observatory is a self-triggered antenna array on top of the ∼2700 m high Mt. Melbourne in Antarctica, designed to detect impulsive geomagnetic emission from extensive air showers induced by ultra-high energy (UHE) particles beyond 1017 eV, including cosmic rays, Earth-skimming tau neutrinos, and particularly, the “ANITA anomalous events” (AAE) from near and below the horizon. The six AAE discovered by the ANITA experiment have signal features similar to tau neutrinos but that hypothesis is in tension either with the interaction length predicted by Standard Model or with the flux limits set by other experiments. Their origin remains uncertain, requiring more experimental inputs for clarification.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

First light from beam neutrinos on an LAPPD in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is both a physics experiment and a technology testbed for next-generation light-based neutrino detection. In this paper, we report the first demonstration of a fully integrated Large Area Picosecond Photodetector (LAPPD) operating in a running neutrino beam experiment. Particular focus is given to the design, commissioning, and successful deployment of the Packaged ANNIE LAPPD (PAL), a waterproof, self-triggering module incorporating fast waveform digitization and precision timing synchronized to the ANNIE detector subsystems. We identify beam-correlated LAPPD data frames consistent with charged-current neutrino interactions observed in multiple detector subsystems, establishing the first detection of neutrino-induced Cherenkov light with an LAPPD. These results validate the system-level performance of LAPPDs under realistic experimental conditions — including long-term stability, timing synchronization, and event matching with conventional PMT and muon detector systems — marking a critical step toward their deployment in future large-scale neutrino and particle detectors.

Cherenkov detectors↗
DOE OSTI2021

Directional Laplacian Centrality for Cyber Situational Awareness

Cyber operations is drowning in diverse, high-volume, multi-source data. To get a full picture of current operations and identify malicious events and actors, analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure, we show how it identifies the most central vertices in standard network datasets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure’s sensitivity to two types of injected attack profiles and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in pp collisions at s=13TeV with the ATLAS detector

A model-agnostic search for Beyond the Standard Model physics is presented, targeting final states with at least four light leptons (electrons or muons). The search regions are separated by event topology and unsupervised machine learning is used to identify anomalous events in the full 140 fb-1$$^{-1}$$ of proton–proton collision data collected with the ATLAS detector during Run 2. No significant excess above the Standard Model background expectation is observed. Model-agnostic limits are presented in each topology, along with limits on several benchmark models including vector-like leptons, wino-like charginos and neutralinos, or smuons. Limits are set on the flavourful vector-like lepton model for the first time.

Aad, G↗
DOE OSTI2021

Search for low-energy neutrinos from astrophysical sources with Borexino

We report on searches for neutrinos and antineutrinos from astrophysical sources performed with the Borexino detector at the Laboratori Nazionali del Gran Sasso in Italy. Electron antineutrinos ( $\bar{ν}_e$ ) are detected in an organic liquid scintillator through the inverse β -decay reaction. In the present work we set model-independent upper limits in the energy range 1.8-16.8 MeV on neutrino fluxes from unknown sources that improve our previous results, on average, by a factor 2.5. Using the same data set, we first obtain experimental constraints on the diffuse supernova $\bar{ν}_e$ fluxes in the previously unexplored region below 8 MeV. A search for $\bar{ν}_e$ in the solar neutrino flux is also presented: the presence of $\bar{ν}_e$ would be a manifestation of a non-zero anomalous magnetic moment of the neutrino, making possible its conversion to antineutrinos in the strong magnetic field of the Sun. We obtain a limit for a solar $\bar{ν}_e$ flux of 384 cm -2 s -1 (90% C.L.), assuming an undistorted solar 8 B neutrinos energy spectrum, that corresponds to a transition probability pνe→$\bar{ν}_e$< 7.2 ×10-5 (90% C.L.) for E $\bar{ν}_e$ > 1.8 MeV.At lower energies, by investigating the spectral shape of elastic scattering events, we obtain a new limit on solar 7 Be- ν e conversion into $\bar{ν}_e$ of pνe→$\bar{ν}_e$< 0.14 (90% C.L.) at 0.862 MeV. Last, we investigate solar flares as possible neutrino sources and obtain the strongest up-to-date limits on the fluence of neutrinos of all flavor neutrino below 3–7 MeV. Assuming the neutrino flux to be proportional to the flare’s intensity, we exclude an intense solar flare as the cause of the observed excess of events in run 117 of the Cl-Ar Homestake experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗
DOE OSTI2025

Thermal Dynamics and Lithium Plating Detection in High‐Power Li‐Ion Batteries for eVTOL Applications

The rapid electrification of aerial transportation is driving the need for high‐performance Li‐ion batteries that can operate reliably under stringent thermal and safety constraints. The unique mission profile of electric Vertical Take‐off and Landing (eVTOL) aircraft necessitates a focused investigation into the thermal behavior and safety characteristics of these batteries. Here, in this study, operando isothermal microcalorimetry is employed to examine the thermal evolution of Li‐ion batteries under cycling conditions representative of eVTOL operations. These findings reveal that high‐power discharge events—such as those during take‐off and landing—shift the thermal response toward exothermic behavior, in contrast to the typically endothermic response expected under near‐equilibrium cycling conditions. Additionally, the results suggest that advanced electrolyte formulations may help suppress excess heat generation, thereby improving battery safety. Notably, the calorimetric results exhibit a distinct thermal signature associated with lithium plating, offering a potential diagnostic for detecting Li plating during eVTOL operation. Overall, this study demonstrates the utility of isothermal microcalorimetry as a valuable tool for assessing thermal risks in Li‐ion batteries for eVTOL applications, and highlights the importance of targeted design strategies to mitigate safety hazards during high‐power demand scenarios.

Li plating↗