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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Global anomaly matching in the higher-dimensional $\mathbb{CP}^{N–1}$ model

Here, we investigate 't Hooft anomalies in the $\mathbb{CP}^{N–1}$ model in space-time dimensions higher than two and identify two types of anomalies: one is a mixed anomaly between the PSU ( N ) flavor-rotation and magnetic symmetries and the other is between the reflection and magnetic symmetries. The latter indicates that even in the absence of the flavor symmetry, the model cannot have a unique gapped ground state as long as the reflection and magnetic symmetries are respected. We also clarified the condition for the 't Hooft anomalies to survive under monopole deformations, which explicitly break the magnetic symmetry down to its discrete subgroup. Besides, we explicitly show how the identified 't Hooft anomalies match in the low-energy effective description of symmetry broken phases—the Néel, U(1) spin liquid, and the valence bond solid phases. An application to the finite-temperature phase diagram of the four-dimensional $\mathbb{CP}^{N–1}$ model is also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Weakly supervised anomaly detection with event-level variables

We introduce a new topology for weakly supervised anomaly detection searches, diobject plus X. In this topology, one looks for a resonance decaying to two standard model particles produced in association with other anomalous event activity (X). This additional activity is used for classification. We demonstrate how anomaly detection techniques which have been developed for dijet searches focusing on jet substructure anomalies can be applied to event-level anomaly detection in this topology. To robustly capture event-level features of multiparticle kinematics, we employ new physically motivated variables derived from the geometric structure of a collision’s phase space manifold. As a proof of concept, we explore the application of this approach to several benchmark signals in the di-𝜏 and di-𝜇 plus X final states. We demonstrate that our anomaly detection approach can reach discovery-level significances for signals that would be missed in a conventional bump-hunt approach.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Detecting Anomalies in Time Series Using Kernel Density Approaches

This paper introduces a novel anomaly detection approach tailored for time series data with exclusive reliance on normal events during training. Our key innovation lies in the application of kernel-density estimation (KDE) to scrutinize reconstruction errors, providing an empirically derived probability distribution for normal events post-reconstruction. This non-parametric density estimation technique offers a nuanced understanding of anomaly detection, differentiating it from prevalent threshold-based mechanisms in existing methodologies. In post-training, events are encoded, decoded, and evaluated against the estimated density, providing a comprehensive notion of normality. In addition, we propose a data augmentation strategy involving variational autoencoder-generated events and a smoothing step for enhanced model robustness. The significance of our autoencoder-based approach is evident in its capacity to learn normal representation without prior anomaly knowledge. Through the KDE step on reconstruction errors, our method addresses the versatility of anomalies, departing from assumptions tied to larger reconstruction errors for anomalous events. Our proposed likelihood measure then distinguishes normal from anomalous events, providing a concise yet comprehensive anomaly detection solution. The extensive experimental results support the feasibility of our proposed method, yielding significantly improved classification performance by nearly 10% on the UCR benchmark data.

Frehner, Robin↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Widening of Wind Stress Anomalies Amplifies ENSO in a Warming Climate

Abstract Climate change simulations generally indicate the strengthening of El Niño–Southern Oscillation (ENSO) sea surface temperature (SST) variability through the twenty-first century, yet a robust physical mechanism explaining this change across different models is still lacking, and the projections for ENSO amplitude exhibit a large spread. Most commonly, changes in the background state of the tropical Pacific are invoked to explain these changes of ENSO. Here, we show that changes in the structure of wind stress anomalies associated with ENSO are potentially as important as these background state changes. Specifically, changes in the magnitude, meridional width, and zonal structure of wind stress anomalies can explain approximately 53% of the intermodel variance in the projected change of ENSO magnitude through the twenty-first century as well as 43% in ENSO periodicity changes. Among these changes in the wind structure, the meridional widening of wind anomalies plays the most important role. To demonstrate that these changes are indeed critical, we develop a hybrid model of ENSO based on the Community Earth System Model, version 2, which incorporates a dynamical ocean coupled to a simplified statistical atmosphere within the tropical Pacific. In the absence of external forcing and corresponding mean-state changes, the imposed changes in wind stress anomalies in this hybrid model result in an increase of ENSO amplitudes of nearly 10% along the equator. Our results are also theoretically supported by a recharge-oscillator model that incorporates the meridional wind structure. Thus, changes in the structure of wind stress anomalies, together with changes in the mean state, likely play a critical role in the projected strengthening of ENSO.

Stuivenvolt-Allen, Jacob [Yale University, New Hav↗
DOE OSTI2023

Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation Inc. that is designed to operate on live cables up to 1000 volts. One of the main conclusions of a previous effort was that cable reflectometry plots can be difficult for humans to analyze due to baseline noise, low or noisy anomaly response peaks, or large responses from cable ends. Detection of cable anomalies for many of these frequencies and test conditions was challenging for manual analysis. This presented an ideal opportunity for ML analysis to distinguish undamaged cable indications from anomalous cable indications. This research discusses application of machine learning (ML) to reflectometry cable test methods. The goal was to assess feasibility to distinguish undamaged cable reflectometry responses from damaged or anomalous cable reflectometry responses. The assessment considered the 3 instruments, multiple frequency bandwidths from each instrument, multiple cable anomalies and test conditions, and both supervised and unsupervised ML approaches. Although approaches and analysis methods were not identical or directly comparable, both outputs were encouraging. The unsupervised prediction weighted accuracy was assessed by instrument and by frequency. It performed better at high frequencies with the highest prediction accuracy of 0.84 for the higher frequency FDR, 0.79 for the 48-MHz LiveWire SSTDR, and 0.77 for 300-MHz PNNL SSTDR. The initial weighted accuracy average across all frequencies for using supervised ML was 0.56 to 0.68. The supervised analysis was repeated with noisier training data removed resulting in weighted accuracies of 0.69 to 0.87. These weighted accuracies are not directly comparable due to differences in the supervised and unsupervised analysis details but do indicate an encouraging trend. Even with limited and unbalanced data, strong prediction accuracies seem encouraging for further work including more data under a wider range of conditions.

42 ENGINEERING↗
DOE OSTI2026

Anomaly Detection in the SBND Experiment Based on Graph Neural Networks

Traditional anomaly detection in SBND experiments require data reconstruction and manual supervision, and thus has the drawbacks of long detection time, being labour intensive and incapable of predicting potential future anomalies. Machine learning models, especially autoencoders, have been widely applied in anomaly detection, and developing an autoencoder for anomaly detection in SBND experiment is going to tremendously improve the efficiency and accuracy of the experiment. The autoencoder has the advantage of automation, efficiency, and can be used to predict future anomalies in the SBND experiment.

Fu, Jiayu [U. Chicago (main)]↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2024

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Abstract We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

The computation of 15 deg and 10 deg equal area block terrestrial free air gravity anomalies

Starting with the set of 23,355 1 deg x 1 deg mean free air gravity anomalies used in Rapp (1972) to form a 5 deg equal area block terrestrial gravity field, the computation of 15 deg equal area block mean free air gravity anomalies is described along with estimates of their standard deviations. A new scheme of an integral division of a 15 deg block into 9 component 300 n. m. blocks, and each 300 n. m. block being subdivided into 25 60 n.mi. blocks, is used. This insures that there is no loss in accuracy, which would have resulted if proportional values according to area were taken of the 5 deg equal area anomalies to form the 15 deg block anomalies. A similar scheme is used for the computation of 10 deg equal area block mean free air gravity anomalies with estimates of their standard deviations. The scheme is general enough to be used for a 30 deg equal area block terrestrial gravity field.

Hajela, D. P.↗
NASA NTRS1974

Analysis of the nature of excessive cosmic radiation in the area of the Brazilian magnetic anomaly at altitudes 250-500km, from Kosmos-225 satellite data

Results are presented from a study of the region of anomalous cosmic radiation in the area of the Brazilian magnetic anomaly at the altitudes 250-500 km, using data measurements taken on the Kosmos-225 satellite (14-29 June 1968). The existence of a stable intensity anomaly discovered in the experiments on the second and third Soviet spacecraft-satellites is confirmed. The total vector of the geomagnetic field at different altitudes was compared with isoline maps. An altitude profile of the South Atlantic anomaly of radiation intensity was obtained, using data from the same instrument. The nature of the anomalies in cosmic radiation intensity over the regions of negative magnetic anomalies is discussed.

Raychenko, L. V.↗
NASA NTRS1974

Direct recovery of mean gravity anomalies from satellite to satellite tracking

The direct recovery was investigated of mean gravity anomalies from summed range rate observations, the signal path being ground station to a geosynchronous relay satellite to a close satellite significantly perturbed by the short wave features of the earth's gravitational field. To ensure realistic observations, these were simulated with the nominal orbital elements for the relay satellite corresponding to ATS-6, and for two different close satellites (one at about 250 km height, and the other at about 900 km height) corresponding to the nominal values for GEOS-C. The earth's gravitational field was represented by a reference set of potential coefficients up to degree and order 12, considered as known values, and by residual gravity anomalies obtained by subtracting the anomalies, implied by the potential coefficients, from their terrestrial estimates. It was found that gravity anomalies could be recovered from strong signal without using any a-priori terrestrial information, i.e. considering their initial values as zero and also assigning them a zero weight matrix. While recovering them from weak signal, it was necessary to use the a-priori estimate of the standard deviation of the anomalies to form their a-priori diagonal weight matrix.

Hajela, D. P.↗
NASA NTRS1975

Apollo experience report: Flight anomaly resolution

The identification of flight anomalies, the determination of their causes, and the approaches taken for corrective action are described. Interrelationships of the broad range of disciplines involved with the complex systems and the team concept employed to ensure timely and accurate resolution of anomalies are discussed. The documentation techniques and the techniques for management of anomaly resolution are included. Examples of specific anomalies are presented in the original form of their progressive documentation. Flight anomaly resolution functioned as a part of the real-time mission support and postflight testing, and results were included in the postflight documentation.

Lobb, J. D.↗
NASA NTRS1976

Analysis of spacecraft anomalies

The anomalies from 316 spacecraft covering the entire U.S. space program were analyzed to determine if there were any experimental or technological programs which could be implemented to remove the anomalies from future space activity. Thirty specific categories of anomalies were found to cover nearly 85 percent of all observed anomalies. Thirteen experiments were defined to deal with 17 of these categories; nine additional experiments were identified to deal with other classes of observed and anticipated anomalies. Preliminary analyses indicate that all 22 experimental programs are both technically feasible and economically viable.

Bloomquist, C. E.↗
NASA NTRS1977

Revised estimation of 550-km times 550-km mean gravity anomalies

The calculation of 550-km x 550-km mean gravity anomalies from 1 degree x 1 degree mean free-air gravimetry data is discussed. The block estimate procedure developed by Kaula is used to obtain 1,504 of the 1,654 possible mean block anomalies. The estimated block anomalies calculated from 1 deg x 1 deg mean anomalies referred to the reference ellipsoid and from 1 degree x 1 degree mean anomalies referred to a 24th-degree-and-order field are compared.

Williamson, M. R.↗
NASA NTRS1978

Isotopic anomalies in solar system material - What can they tell us

Three relatively recently discovered anomalies are considered. The goal of isotopic research is to understand what phenomena are responsible for the observed isotopic anomalies, and thereby to determine the initial solar system isotopic composition. Until recently there has been no measured isotopic ratios which could not be understood in the context of reasonable physical or chemical processes acting to alter a uniform initial solar isotopic composition. This situation changed in 1969 with the discovery by Black and Pepin of an unusual neon isotopic composition in certain carbonaceous meteories. This unusual composition was later designated as Neon E. An unusual or anomalous oxygen isotopic composition was discovered by Clayton et al. (1973). A magnesium isotopic anomaly was discovered by Gray and Compston (1974), and Lee and Papanastassiou (1974). The three isotopic anomalies are discussed, emphasizing particularly the experimental evidence, possible causes for the observed isotopic composition and finally, possible implications of these anomalies with regard to models of solar system formation and evolution.

Black, D. C.↗
NASA NTRS1978

Lunar Bouguer gravity anomalies - Imbrian age craters

The Bouguer gravity of mass anomalies associated with four Imbrian age craters, analyzed in the present paper, are found to differ considerably from the values of the mass anomalies associated with some young lunar craters. Of the Imbrian age craters, only Piccolomini exhibits a negative gravity anomaly (i.e., a low density region) which is characteristic of the young craters studied. The Bouguer gravity anomalies are zero for each of the remaining Imbrian age craters. Since, Piccolomini is younger, or at least less modified, than the other Imbrian age craters, it is suggested that the processes responsible for the post-impact modification of the Imbrian age craters may also be responsible for removing the negative mass anomalies initially associated with these features.

Dvorak, J.↗
NASA NTRS1979

Gravity anomaly and geoid undulation results in local areas from GEOS-3 altimeter data

The adjusted GEOS-3 altimeter data, taken as averages within a data frame, have been used to construct free air anomaly and geoid undulation profiles and maps in areas of geophysical interest. Profiles were constructed across the Philippine Trench (at a latitude of 6 deg) and across the Bonin Trench (at a latitude of 28 deg). In the latter case an anomaly variation of 443 mgals in 143 km was derived from the altimeter data. These variations agreed reasonably with terrestrial estimates, considering the predicted point accuracy was about + or - 27 mgals. An area over the Patton Sea mounts was also investigated with the altimeter anomaly field agreeing well with the terrestrial data except for the point directly over the top of the sea mount. It is concluded that the GEOS-3 altimeter data is valuable not only for determining 5 deg and 1 deg x 1 deg mean anomalies, but also can be used to describe more local anomaly variations.

Rapp, R. H.↗