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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Towards Anomaly Detection at the CMS High-Level Trigger System

Traditional trigger strategies in CMS typically rely on model-dependent selections or rigid kinematic cuts, risking the omission of unexpected exotic signatures. To address this, we propose a novel anomaly detection (AD) algorithm for the High-Level Trigger (HLT), designed to serve as a complementary second layer of filtering to the Level-1 AXOL1TL AD algorithm. We employ a transformer-based foundation model trained on a diverse ensemble of Standard Model processes. By combining a joint contrastive and classification objective, and using particle kinematics as inputs, the model learns to map events to a physics-informed latent space where anomalous events are isolated from dominant backgrounds. Preliminary results show that this strategy enhances the signal-to-background ratio across a range of rare SM and BSM scenarios. Furthermore, this work constitutes foundational R&D for the potential implementation of an analogous AD algorithm in the Level-1 trigger system for Phase-2.

Cruz, Roy [U. Wisconsin, Madison (main)] (ORCID:00↗
DOE OSTI2020

Generic FEPs Catalogue and Salt Knowledge Archive

This report describes the development of a comprehensive catalogue of generic features, events, and processes (FEPs) that are potentially important for the post-closure performance of a repository for high-level radioactive waste (HLW) and spent nuclear fuel (SNF) in salt (halite) host rock. The FEPs and other supporting information have been entered into a “SaltFEP” Database. The generic salt repository FEPs include consideration of relevant FEPs from a number of U.S., Dutch, German, and international FEP lists and should be a suitable starting point for any repository program in salt host rock. The salt FEP catalogue and database employ a FEP classification matrix approach that is based on the concept that a FEP is typically a process or event acting upon or within a feature. The FEP matrix provides a two-dimensional structure consisting of a Features/Components axis that defines the “rows” and a Processes/Events axis that defines the “columns” of the matrix. The design of the FEP classification matrix is consistent with repository performance assessment – the Features/Components axis is organized vertically to generally correspond to the direction of potential radionuclide migration (from the waste to the biosphere) and the Processes/Events axis is designed to represent the common two-way couplings between thermal processes and other processes (such as thermal-mechanical or thermal-hydrologic processes). Related FEPs can be easily identified – related FEPs will typically be grouped in a single matrix cell or aligned along a common row (Feature/Component) or column (Process/Event). The online SaltFEP database can be downloaded from www.saltfep.org. It contains the FEP matrix, the FEPs, and the associated processes for each FEP. It provides a starting point to create and document site-specific individual FEPs. Furthermore, the FEP matrix is connected to the Salt Knowledge Archive (SKA), a database of about 20,000 references and documents representing the historical knowledge on radioactive disposal in salt. This work is the result of an ongoing collaboration between researchers in the U.S., the Netherlands, and Germany, and supports the NEA Salt Club Mandate. It builds upon prior work which is documented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Deep learning methods for event verification and image re-purposing detection

Systems and methods herein describe accessing an image, generating a resized image, generating an image feature vector by applying an image classification neural network to the resized image, generating analysis of the image by processing the image feature vector using a machine-learning classifier trained to analyze the image feature vector, and based on the analysis, determining an event that is attributed to the image.

Nataraj, Lakshmanan↗
DOE OSTI2021

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's ν e -appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. Here, the accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Reconstruction of unstable heavy particles using deep symmetry-preserving attention networks

Abstract Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention mechanism, symmetry preserving attention networks (SPA-NET), has been previously applied to top quark pair decays at the Large Hadron Collider which produce only hadronic jets. Here we extend the SPA-NET architecture to consider multiple input object types, such as leptons, as well as global event features, such as the missing transverse momentum. In addition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: a search for$$t\bar{t}H$$ t t ¯ H , a measurement of the top quark mass, and a search for a heavy$${Z}^{{\prime} }$$ Z ′ decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case.

Physics↗
DOE OSTI2022

Distinguishing Unique Earthquakes with Overlapping Signals in Oklahoma

Abstract During routine operations monitoring Oklahoma earthquakes, we found that certain earthquakes occurred closely both in space and time and had overlapping phases at the recording stations. Through further scrutiny and analysis, we determined that rather than being distinctly different earthquakes, some of the earthquakes exhibited multiphase arrivals and longer than expected coda due to unique ray paths that encounter impedance contrasts such as at the sedimentary rock-basement. Of course, some of these events truly were distinct events, which we term overlapping earthquakes, for which perceived coda duration overlaps and obscures the phase arrivals of the second event due to the source proximity in both time and space. We detail our classification scheme to separate the local earthquakes in Oklahoma as single, overlapping earthquakes, or those associated with multiphase arrivals. We forward model seismic wave propagation in a 2D crustal model and develop a methodology that utilizes waveform correlation to distinguish phases from overlapping earthquakes to those from crustal reverberations. Duration analysis shows a more elongated duration, qualitatively similar to the duration produced by overlapping earthquakes, at the sites where multiphase arrivals are observed.

Geochemistry & Geophysics↗
DOE OSTI2022

Linking Extragalactic Transients and Their Host Galaxy Properties: Transient Sample, Multiwavelength Host Identification, and Database Construction

Understanding the preferences of transient types for host galaxies with certain characteristics is key to studies of transient physics and galaxy evolution, as well as to transient identification and classification in the LSST era. Here we describe a value-added database of extragalactic transients—supernovae, tidal disruption events, gamma-ray bursts, and other rare events—and their host galaxy properties. Based on reported coordinates, redshifts, and host galaxies (if known) of events, we cross-identify their host galaxies or most likely host candidates in various value-added or survey catalogs, and compile the existing photometric, spectroscopic, and derived physical properties of the host galaxies in these catalogs. This new database covers photometric measurements from the far-ultraviolet to mid-infrared. Spectroscopic measurements and derived physical properties are also available for a smaller subset of hosts. For our 36,333 unique events, we have cross-identified 13,753 host galaxies using host names, plus 4480 using host coordinates. Besides those with known hosts, there are 18,100 transients with newly identified host candidates. This large database will allow explorations of the connections of transients to their hosts, including a path toward transient alert filtering and probabilistic classification based on host properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Lab-Scale Demonstration of Pipeline Third-Party Damage Classification Using Convolutional Neural Networks

This research aims to mitigate the challenges of field tests for classification of third-party damages by generating a dataset of third-party damage events on a laboratory scale utilizing single mode-multi mode-single mode (SMS) fiber acoustic sensor. The sound samples representative of various third-party activities, such as vehicle movements, excavation, and digging, were sourced from open-source databases. These samples were then played through a speaker in proximity to an SMS sensor, and the resultant fiber acoustic vibration data were recorded for each event. This process yielded a collection of 200 samples across 13 distinct third-party events. Convolutional Neural Networks (CNNs) were employed to classify these samples into their respective categories, and an accuracy exceeding 97% was obtained from our results.

Bukka, Sandeep Reddy↗
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 OSTI2022

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

ABSTRACT Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

AT 2018dyk: tidal disruption event or active galactic nucleus? Follow-up observations of an extreme coronal line emitter with the Dark Energy Spectroscopic Instrument

We present fresh insights into the nature of the tidal disruption event (TDE) candidate AT 2018dyk. AT 2018dyk has sparked a debate in the literature around its classification as either a bona-fide TDE or as an active galactic nucleus (AGN) turn-on state change. A new follow-up spectrum taken with the Dark Energy Spectroscopic Instrument, in combination with host-galaxy analysis using archival SDSS–MaNGA data, supports the identification of AT 2018dyk as a TDE. Specifically, we classify this object as a TDE that occurred within a gas-rich environment, which was responsible for both its mid-infrared (MIR) outburst and development of Fe coronal emission lines. Comparison with the known sample of TDE-linked extreme coronal line emitters (TDE-ECLEs) and other TDEs displaying coronal emission lines (CrL-TDEs) reveals similar characteristics and shared properties. For example, the MIR properties of both groups appear to form a continuum with links to the content and density of the material in their local environments. This includes evidence for a MIR colour–luminosity relationship in TDEs occurring within such gas-rich environments, with those with larger MIR outbursts also exhibiting redder peaks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

On the Breakdown of ENSO's Relationship With Thermocline Depth in the Central-Equatorial Pacific

Recent classification of El Nino-Southern Oscillation (ENSO) into two types, Eastern (EP) and Central (CP) events, has highlighted the importance of the central Pacific. We show here that the local correlation between ENSO subsurface temperatures (T sub ) in the upper 100-m and thermocline depth anomalies breaks down in the central equatorial Pacific, whereas T sub remains well correlated with sea surface height anomalies. This observed difference in the central equatorial Pacific is simulated by almost all climate models. It arises from a vertically slanted Tsub anomaly structure that is unique to the central equatorial Pacific. We show that this feature is an adiabatic response to wind-driving that is even present in a linear dynamic model, as long as the model has enough baroclinic modes to adequately represent the observed vertical complexity. Our findings have implications for better understanding the different importance of thermocline feedback in CP and EP events.

58 GEOSCIENCES↗
DOE OSTI2026

Feedback and Oscillations: Constructing Feedback Systems for Root Cause Analysis of Oscillations in Power Grids

Dynamic phenomena linked to inverter-based resources (IBRs) have gained global attention. Several IBR-induced dynamics have caused bulk power system-connected wind or solar power plants to trip, and some have even led to widespread outages. In addition, many oscillations have been observed involving IBR power plants. In 2023, the IEEE Power & Energy Society (PES) IBR Subsynchronous Oscillations (SSO) task force published a journal article, “Real-World Subsynchronous Oscillation Events in Power Grids With High Penetrations of Inverter-Based Resources,” in which 19 IBR oscillation events were examined for their causation. Earlier in 2020, another PES task force article, “Definition and Classification of Power System Stability-Revisited & Extended,” authored by prominent academics, introduced converter-driven stability as a new category of stability. The international power grid industry community also took action by publishing the CIGRE Green Book, Power System Dynamic Modelling and Analysis in Evolving Networks (led by Babak Badrzadeh and Zia Emin) in 2024. In August 2024, the Energy Systems Integration Group (ESIG) released a practical guide led by Nick Miller, “Diagnosis and Mitigation of Observed Oscillations in IBR-Dominant Power System: A Practical Guide.” The goal of the guide is to assist practicing engineers in making initial judgments and conducting detailed analyses about oscillations. Finally, when addressing the classification of stability and oscillations, the guide emphasizes a causality-based taxonomy for grouping, such as voltage control-induced oscillations, synchronization-induced oscillations, and frequency or active power control-induced oscillations.

Fan, Lingling [Univ. of South Florida, Tampa, FL (↗
DOE OSTI2023

The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.

convolutional neural networks↗