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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

Abstract The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.

plasma instabilities↗
DOE OSTI2025

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗
DOE OSTI2023

Moment tensor event identification for collapses

SUMMARY We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated.

58 GEOSCIENCES↗
DOE OSTI2025

First Event-by-Event Identification of Cherenkov Radiation from Sub-Mev Particles in Liquid Argon

This Letter reports the event-by-event observation of Cherenkov light from sub-MeV electrons in a high scintillation light-yield liquid argon detector by the coherent CAPTAIN-Mills (CCM) experiment. The CCM200 detector, located at Los Alamos National Laboratory, instruments seven tons (fiducial volume) of liquid argon with 200 eight-inch photomultiplier tubes, 80% of which are coated in a wavelength-shifting material and the remaining 20% are uncoated. In the prompt time region of an event, defined as - 6 ≤ t < 0 ns relative to the event start time t = 0 , the uncoated photomultiplier tubes are primarily sensitive to visible Cherenkov photons. Using gamma rays from a Na 22 source for production of sub-MeV electrons, we isolated prompt Cherenkov light with > 5 σ confidence and developed a selection to obtain a low-background electromagnetic sample. This is the first event-by-event observation of Cherenkov photons from sub-MeV electrons in a high-yield scintillator detector and represents a milestone in low-energy particle detector development.

Aguilar-Arevalo, A. A. [UNAM, Mexico]↗
DOE OSTI2024

A Data-Driven Framework for Power System Event Type Identification via Safe Semi-Supervised Techniques

Herein this paper investigates the use of phasor measurement unit (PMU) data with deep learning techniques to construct real-time event identification models for transmission networks. Increasing penetration of distributed energy resources represents a great opportunity to achieve decarbonization, as well as challenges in systematic situational awareness. When high-resolution PMU data and sufficient manually recorded event labels are available, the power event identification problem is defined as a statistical classification problem that can be solved by numerous cutting-edge classifiers. However, in real grids, collecting tremendous high-quality event labels is quite expensive. Utilities frequently have a large number of event records without in-depth details (i.e., unlabeled events). To bridge this gap, we propose a novel semi-supervised learning-based method to improve the performance of event classifiers trained with a limited number of labeled events by exploiting the information from massive unlabeled events. In other words, compared to existing data-driven methods, our method requires only a small portion of labeled data to achieve a similar level of accuracy. Meanwhile, this work discusses and addresses the performance degradation caused by class distribution mismatch between the training set and the real applications. Based on the proposed safe learning mechanism, our model does not directly use all unlabeled events during model training, but selectively uses them through a comprehensive evaluation procedure. Numerical studies on a sizable PMU dataset have been used to validate the performance of the proposed method.

42 ENGINEERING↗
DOE OSTI2026

Post-Event Fault Identification with Machine Learning for Protection System Validation

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 accidental improper relay settings or deliberate 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 their performance falls within expectations. Relays that fail to isolate a fault or trip when there is no system disturbance can be flagged for settings review in situations where this behavior may not have been noticed due to manual restoration or backup protection operations. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by identifying fault 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 OSTI2022

Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2023

Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in the cold nuclear matter environment in electron-nucleus collisions. We establish first benchmarks and contrast the estimated performance of flavor tagging at the EIC with that at the Large Hadron Collider. We perform studies relevant to aspects of detector design including particle identification, charge information, and minimum transverse momentum capabilities. Additionally, we study the impact of using full event information instead of using only information associated with the identified jet. These methods can be deployed either on suitably accurate Monte Carlo event generators, or, for several applications, directly on experimental data. We provide an outlook for ultimately connecting these machine learning-based methods with first principles calculations in quantum chromodynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor

Moment tensors (MTs) have long been used in earthquake and explosion source analysis, and there has been a renewed interest in how they can inform us about the seismic source, particularly in the geophysical monitoring community due to its application in event identification and yield analysis. However, parameter uncertainties in seismic MT inversion are rarely available. The inverse procedure often does not quantify MT model errors such as event location, data noise and Earth model that are essential for estimating solution robustness. To address this need, we propose to adopt the Bayesian probabilistic framework to incorporate uncertainties in MT inversions. In this study, we present the theoretical background of a probabilistic Bayesian framework for MT inversion accounting for model and measurements errors and illustrate the implementation of the method using a synthetic example.

58 GEOSCIENCES↗
DOE OSTI2023

Improving the NOvA 3-Flavour Neutrino Oscillation Analysis

NOvA is a long-baseline neutrino experiment studying neutrino oscillations, a quantum mechanical interference phenomena where the observed neutrino flavour differs from that measured earlier, stemming from neutrino mass and flavour states mixing. NOvA consists of two functionally identical tracking calorimeter detectors deployed in the Fermilab NuMI beam. Both detectors are placed 14.6 mrad off the beam axis to achieve a narrow energy peak at 1.8 GeV at an oscillation maximum. The NOvA 3-flavour oscillation analysis measures the neutrino oscillation parameters sin2θ23 and ∆m232 as well as sets limit to δCP , the octant of θ23 and the sign of ∆m32. The event selection for 3-flavour neutrino oscillation analysis ensures the maximum quantity of signal is made available and a minimal amount of background is present. The current disappearance analysis selection has an efficiency of 80% for selecting νμ CC events, and with some improvements additional events could be recovered into the analysis to improve the sensitivity to the aforementioned oscillation parameters. In an effort to recover the currently rejected signal event to the analysis, these events were trained in a classification neural network. The aim of the network was to divide the data into signal (νμ CC events) and background (NC and νe CC events). The highest performing network gave an additional figure of merit gain of 2.34 increasing the sensitivity by 4.3% in effective POT equal to 33 days of additional data taking. This was compared to changing the current particle identification event selection cuts, the best result out of the tested cut combinations gave an additional FOM of 3.36 equal to 3.7% increase in effective POT equivalent to 28 days of data taking.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Streaming Readout and Data-Stream Processing With ERSAP

With the exponential growth in the volume and complexity of data generated at high-energy physics and nuclear physics research facilities, there is an imperative demand for innovative strategies to process this data in real or near-real-time. Given the surge in the requirement for high-performance computing, it becomes pivotal to reassess the adaptability of current data processing architectures in integrating new technologies and managing streaming data. This paper introduces the ERSAP framework, a modern solution that synergizes flow-based programming with the reactive actor model, paving the way for distributed, reactive, and high performance in data stream processing applications. Additionally, we unveil a novel algorithm focused on time-based clustering and event identification in data streams. The efficacy of this approach is further exemplified through the data-stream processing outcomes obtained from the recent beam tests of the EIC prototype calorimeter at DESY.

Vardan, Gyurjyan↗
DOE OSTI2023

3D track reconstruction of low-energy electrons in the MIGDAL low pressure optical time projection chamber

Here, we demonstrate three-dimensional track reconstruction of electrons in a low pressure (50 Torr) optical TPC consisting of two glass GEMs with an ITO strip readout in CF 4 and CF 4 /Ar mixtures. The reconstructed tracks show a variety of event topologies, including short tracks from photoelectrons induced by 55 Fe 5.9 keV X-rays and long tracks from gamma ray interactions and beta decays. Algorithms for event identification and track ridge detection are discussed as well as multiple methods for integrating information from the camera image and ITO waveforms with the goal of full 3D reconstruction of the track.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cosmic Background Rejection of the ICARUS experiment at Fermilab

The Short Baseline Neutrino program at Fermilab aims to explore significant regions of parameter space, applicable to sterile neutrinos at the eV mass scale, as suggested by existing experimental anomalies. To this purpose it exploits Liquid Argon Time Projection Chamber detectors located along the Booster Neutrino Beamline to measure both νe appearance and νµ disappearance: the Short Baseline Neutrino Detector and the ICARUS-T600 detector at 110 and 600 m from the neutrino source, respectively. The ICARUS T-600 Far Detector, located at shallow depth, is surrounded by a Cosmic Ray Tagger system to mitigate the cosmic ray background. On average ~ 11 muon tracks are expected to cross the detector during the ~ 1 ms drift time. The cosmic ray tagger is composed of plastic scintillator bars, ensuring near 4π coverage of the detector aiming at tagging cosmic muons and thus reject 𝛾s produced by muon interactions in the surrounding materials that can generate an electromagnetic showers mimicking a νe signal. The system allows one to disentangle cosmic rays from particles originated in a neutrino interaction inside the detector by measuring their position and crossing time. A synchronization of the cosmic ray tagger with the ICARUS photon detection system with a nanosecond accuracy allows one to reject cosmic particles recorded during the beam spill and thus select an enriched sample of neutrino triggered events ahead of the event reconstruction. An overview of the cosmic ray tagger system as well as its role in the neutrino events identification and cosmic background rejection will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗