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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.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗
DOE OSTI2023

Cybersecurity Anomaly Detection in SCADA-Assisted OT Networks Using Ensemble-Based State Prediction Model

The cybersecurity threats of power system gradually grow due to the increased sophisticated interactions between Information Technology (IT) and Operational Technology (OT) networks. False data injection attack (FDIA) that aims to compromise the Supervisory Control and Data Acquisition (SCADA) measurement and disturb the system operation is one of such cyber threats. Such attacks can potentially lead to significant operational issues at the control centers and substations, and hence, result in severe physical consequences. To avoid catastrophic failure across the power grid resulting from these attacks, it is essential to arm the OT network with real-time vulnerability assessment tools. To this end, this paper outlines various drawbacks of the Purdue architecture model to defend against cyberattacks in the OT network. Furthermore, a novel ensemble-based state prediction model is proposed to detect cybersecurity anomalies in SCADA assisted OT networks. The proposed model uses control center level generation and load forecasts, scheduled, and forced outages, power flow solutions, and the substation level historical data. The hypothesis of the proposed scheme relies on the fact that additional control center and substation data can hardly be accessed and compromised by attackers. One of the vital features of the proposed scheme is an hour-ahead prediction of the operational feasibility of the SCADA measurement range at the control center and substation in real time helps in detecting anomalies in measurements across both substation and the control center.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants

Historically, nuclear power plants have operated predominantly at or near full power, meaning that data driven anomaly detection methods can likely perform well at full power operations. This presents a challenge when the power drops (referred to as a transient) and may result in false alarms due to the lack of historical data at those new power levels. The current approach to handling this challenge is to turn detectors off during transients, which makes it impossible to use the algorithms to detect anomalies during these periods, i.e., causing missed detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗
NASA NTRSDate not supplied

Possible Role of Mixing on Water Vapor and Ozone Anomalies During RF05

We investigate the possible role of mixing on the observed variations in water vapor and ozone anomalies during DCOTSS 29 July 2021 flight. Preliminary results indicate that enhanced water vapor is more often associated with enhanced ozone, opposite of what one might expect during convective plume encounters. We use the turbulence eddy dissipation rate as a proxy of mixing to show that when water vapor is significantly enhanced (> 30 ppmv), water vapor anomalies are negatively correlated with mixing strength: strong turbulence and large mixing are associated with relatively dry air. The mechanism behind this relationship as well as the origins of the observed turbulence and mixing will be investigate in the figure.

Mixing↗
NASA NTRS2022

Irrigation Characterization Improved by the Direct Use of SMAP Soil Moisture Anomalies Within a Data Assimilation System

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

soil moisture↗
NASA NTRSDate not supplied

Reassessing Post-Eruption Precipitation Anomalies in the Context of Natural Variability

Several studies have reported anomalous precipitation in the years that follow large volcanic eruptions. Here we assess precipitation anomalies in post-eruption years according to their rarity in non-volcanic periods. Using global simulations of climate over the last millennium, we reevaluate the response of precipitation to volcanic aerosols in terms of novel metrics. In this approach, the magnitude of the precipitation response is contextualized through comparisons to internal variability. We also examine the connections between global and regional-scale precipitation anomalies. Finally, we assess to what extent a unique ‘fingerprint’ of eruptions can be seen in the observational precipitation record in spite of concurrent internal variability.

volcanic eruptions↗
DOE OSTI2026

Graph theory inspired anomaly detection at the LHC

Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly detection, using the LHC Olympics dataset as a benchmark. By representing jet constituents as a graph, we introduce a method to systematically control the information available to the model through sparse graph constructions that serve as physically motivated inductive biases. Specifically, (1) we construct graph autoencoders based on locally rigid Laman graphs and globally rigid unique graphs, and (2) we explore the clustering of jet constituents into subjets to interpolate between high- and low-level input representations. We obtain the best performance, measured in terms of the Significance Improvement Characteristic curve for an intermediate level of subjet clustering and certain sparse unique graph constructions. We further investigate the role of graph connectivity in jet classification tasks. Our results demonstrate the potential of leveraging graph-theoretic insights to refine and increase the interpretability of machine learning tools for collider experiments.

Automation↗
DOE OSTI2021

Unified framework for B -anomalies, muon g – 2 and neutrino masses

We present a model of radiative neutrino masses which also resolves anomalies reported in B -meson decays, \( {R}_{D^{\left(\ast \right)}} \) and \( {R}_{K^{\left(\ast \right)}} \) , as well as in muon g – 2 measurement, Δ a μ . Neutrino masses arise in the model through loop diagrams involving TeV-scale leptoquark (LQ) scalars R 2 and S 3 . Fits to neutrino oscillation parameters are obtained satisfying all flavor constraints which also explain the anomalies in \( {R}_{D^{\left(\ast \right)}} \) , \( {R}_{K^{\left(\ast \right)}} \) and Δ a μ within 1 σ . An isospin-3/2 Higgs quadruplet plays a crucial role in generating neutrino masses; we point out that the doubly-charged scalar contained therein can be produced in the decays of the S 3 LQ, which enhances its reach to 1.1 (6.2) TeV at \( \sqrt{s} \) = 14 TeV high-luminosity LHC ( \( \sqrt{s} \) = 100 TeV FCC-hh). We also present flavor-dependent upper limits on the Yukawa couplings of the LQs to the first two family fermions, arising from non-resonant dilepton ( pp → ℓ + ℓ – ) processes mediated by t -channel LQ exchange, which for 1 TeV LQ mass, are found to be in the range (0 . 15 – 0 . 36). These limits preclude any explanation of \( {R}_{D^{\left(\ast \right)}} \) through LQ-mediated B -meson decays involving ν e or ν μ in the final state. We also find that the same Yukawa couplings responsible for the chirally-enhanced contribution to Δ a μ give rise to new contributions to the SM Higgs decays to muon and tau pairs, with the modifications to the corresponding branching ratios being at (2–6)% level, which could be tested at future hadron colliders, such as HL-LHC and FCC-hh.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Anomalies in global SMEFT analyses. A case study of first-row CKM unitarity

Recent developments in the Standard Model analysis of semileptonic charged-current processes involving light quarks have revealed ~ 3σ tensions in Cabibbo universality tests involving meson, neutron, and nuclear beta decays. In this paper, we explore beyond the Standard Model explanations of this so-called Cabibbo Angle Anomaly in the framework of the Standard Model Effective Field Theory (SMEFT), including not only low-energy charged current processes (‘L’), but also electroweak precision observables (‘EW’) and Drell-Yan collider processes (‘C’) that probe the same underlying physics across a broad range of energy scales. The resulting ‘CLEW’ framework not only allows one to test explanations of the Cabibbo Angle Anomaly, but is set up to provide near model-independent analyses with minimal assumptions on the flavor structure of the SMEFT operators. Besides the global analysis, we consider a large number of simpler scenarios, each with a subset of SMEFT operators, and investigate how much they improve upon the Standard Model fit. We find that the most favored scenarios, as judged by the Akaike Information Criterion, are those that involve right-handed charged currents. Additional interactions, namely oblique operators, terms modifying the Fermi constant, and operators involving right-handed neutral currents, play a role if the CDF determination of the W mass is included in the analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Non-resonant anomaly detection with background extrapolation

Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics scenarios are relatively under-explored and may arise from off-shell effects or final states with significant missing energy. In this paper, we extend a class of weakly supervised anomaly detection strategies developed for resonant physics to the non-resonant case. Machine learning models are trained to reweight, generate, or morph the background, extrapolated from a control region. A classifier is then trained in a signal region to distinguish the estimated background from the data. The new methods are demonstrated using a semi-visible jet signature as a benchmark signal model, and are shown to automatically identify the anomalous events without specifying the signal ahead of time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Electric dipole moments in a leptoquark scenario for the B-physics anomalies

Vector leptoquarks can address the lepton flavor universality anomalies in decays associated with the b → cℓν and b → sℓℓ transitions, as observed in recent years. While not required to explain the anomalies, these leptoquarks generically yield new sources of CP violation. In this paper, we explore constraints and discovery potential for electric dipole moments (EDMs) in leptonic and hadronic systems. We provide the most generic expressions for dipole moments induced by vector leptoquarks at one loop. We find that O(1) CP-violating phases in tau and muon couplings can lead to corresponding EDMs within reach of next-generation EDM experiments, and that existing bounds on the electron EDM already put stringent constraints on CP-violating electron couplings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Exact-WKB, complete resurgent structure, and mixed anomaly in quantum mechanics on S 1

We investigate the exact-WKB analysis for quantum mechanics in a periodic potential, with N minima on S 1 . We describe the Stokes graphs of a general potential problem as a network of Airy-type or degenerate Weber-type building blocks, and provide a dictionary between the two. The two formulations are equivalent, but with their own pros and cons. Exact-WKB produces the quantization condition consistent with the known conjectures and mixed anomaly. The quantization condition for the case of N-minima on the circle factorizes over the Hilbert sub-spaces labeled by discrete theta angle (or Bloch momenta), and is consistent with ’t Hooft anomaly for even N and global inconsistency for odd N. By using Delabaere-Dillinger-Pham formula, we prove that the resurgent structure is closed in these Hilbert subspaces, built on discrete theta vacua, and by a transformation, this implies that fixed topological sectors (columns of resurgence triangle) are also closed under resurgence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Resonant anomaly detection with multiple reference datasets

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWoLa) and Simulation Assisted Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset. They cannot take advantage of commonly available multiple datasets and thus cannot fully exploit available information. In this work, we propose generalizations of CWoLa and SALAD for settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings with realistic and synthetic data. As an added benefit, our generalizations enable us to provide finite-sample guarantees, improving on existing asymptotic analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Remarks on geometric engineering, symmetry TFTs and anomalies

Abstract Geometric engineering is a collection of tools developed to establish dictionaries between local singularities in string theory and (supersymmetric) quantum fields. Extended operators and defects, as well as their higher quantum numbers captured by topological symmetries, can be encoded within geometric engineering dictionaries. In this paper we revisit and clarify aspects of these techniques, with special emphasis on ’t Hooft anomalies, interpreted from the SymTFT perspective as obstructions to the existence of Neumann boundary conditions. These obstructions to gauging higher symmetries are captured via higher link correlators for the SymTFT on spheres. In this work, we give the geometric engineering counterpart of this construction in terms of higher links of topological membranes. We provide a consistency check in the context of 5D SCFTs with anomalous 1-form symmetries, where we give two independent derivations of the anomaly in terms of higher links, one purely field theoretical and the other purely geometrical. Along the way, we also recover the construction of non-invertible duality defects in 4D$$ \mathcal{N} $$ N = 4 SYM from a geometric engineering perspective.

Physics↗
DOE OSTI2020

Interface conformal anomalies

We consider two d ≥ 2 conformal field theories (CFTs) glued together along a codimension one conformal interface. The conformal anomaly of such a system contains both bulk and interface contributions. In a curved-space setup, we compute the heat kernel coefficients and interface central charges in free theories. The results are consistent with the known boundary CFT data via the folding trick. In d = 4, two interface invariants generally allowed as anomalies turn out to have vanishing interface charges. These missing invariants are constructed from components with odd parity with respect to flipping the orientation of the defect. We conjecture that all invariants constructed from components with odd parity may have vanishing coefficient for symmetric interfaces, even in the case of interacting interface CFT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗