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DOE OSTI2023

Comparison of Supervised and Un-Supervised Machine Learning Algorithms for Threat Detection and Scintillator Performance for Radiation Portal Monitoring

Following the events of September 11, 2001, international border crossing have been equipped with radiation portal monitors (RPMs) to identify illicit radioactive material. Polyvinyl toluene (PVT) scintillators are commonly used due to their low cost and reasonable maintainability, however they offer low spectral resolution. Despite the fact that over twenty years has transpired since this event, radioisotopes are still typically identified by hand-crafted classification algorithms, e.g., total counts or energy windowing, and exhibit relatively poor performance in detecting threats at the low false alarm rates required to support the stream of commerce. While some improvement to performance has been realized via the use of supervised machine learning, these classification algorithms typically utilize simulations in lieu of real data due to the sparsity of data for one or more classes. Accordingly, the performance of these algorithms is somewhat less than optimal when examining experiments or simulations with model mismatch. Consequently, in this work, we examine the application of a number of unsupervised machine learning, anomaly detection based algorithms, to circumvent the inverse crime when analyzing spectroscopy data for RPMs. We also compare anomaly detection results with those obtained via the use of supervised classification detection ML algorithms when model mismatch is introduced between the simulated threat items utilized for training/testing. Finally, we compared the performance of the PVT scintillators to those obtained with higher resolution detectors using both anomaly detection and supervised classification algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

Invasion and rapid adaptation of guppies ( Poecilia reticulata ) across the Hawaiian Archipelago

How much does natural selection, as opposed to genetic drift, admixture, and gene flow, contribute to the evolution of invasive species following introduction to a new environment? Here we assess how evolution can shape biological invasions by examining population genomic variation in non-native guppies (Poecilia reticulata) introduced to the Hawaiian Islands approximately a century ago. By examining 18 invasive populations from four Hawaiian islands and four populations from the native range in northern South America, we reconstructed the history of introductions and evaluated population structure as well as the extent of ongoing gene flow across watersheds and among islands. Patterns of differentiation indicate that guppies have developed significant population structure, with little natural or human-mediated gene flow having occurred among populations following introduction. Demographic modeling and admixture graph analyses together suggest that guppies were initially introduced to O‘ahu and Maui and then translocated to Hawai‘i and Kaua‘i. We detected evidence for only one introduction event from the native range, implying that any adaptive evolution in introduced populations likely utilized the genetic variation present in the founding population. Environmental association tests accounting for population structure identified loci exhibiting signatures of adaptive variation related to predators and landscape characteristics but not nutrient regimes. When paired with high estimates of effective population sizes and detectable population structure, the presence of environment-associated loci supports the role of natural selection in shaping contemporary evolution of Hawaiian guppy populations. Our findings indicate that local adaptation may engender invasion success, particularly in species with life histories that facilitate rapid evolution. Finally, evidence of low gene flow between populations suggests that removal could be an effective approach to control invasive guppies across the Hawaiian archipelago.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Demystifying Cyberattacks: Potential for Securing Energy Systems With Explainable AI : Preprint

Modernization of energy systems has led to in- creased interactions among multiple critical infrastructures and diverse stakeholders making the challenge of operational decision making more complex and at times beyond cognitive capabilities of human operators. The state-of-the-art machine learning and deep learning approaches show promise of supporting users with complex decision-making challenges, such as those occurring in our rapidly transforming cyber-physical energy systems. However, successful adoption of data-driven decision support technology for critical infrastructure will be dependent on the ability of these technologies to be trustworthy and contextually interpretable. In this paper, we investigate the feasibility of implementing XAI for interpretable detection of cyberattacks in the energy system. Leveraging a proof-of-concept simulation use case of detection of a data falsification attack on a photovoltaic system using XGBoost algorithm, we demonstrate how Local Interpretable Model-Agnostic Explanations (LIME), a flavor XAI approach, can help provide contextual and actionable interpretation of cyberattack detection.

artificial intelligence↗

DOE OSTI2024

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (M w ≤ 5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.

58 GEOSCIENCES↗

DOE OSTI2022

Challenges for unsupervised anomaly detection in particle physics

Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain types of data (background) but not others (signals). In this paper, we study some challenges associated with variational autoencoders, such as the dependence on hyperparameters and the metric used, in the context of anomalous signal (top and W) jets in a QCD background. We find that the hyperparameter choices strongly affect the network performance and that the optimal parameters for one signal are non-optimal for another. In exploring the networks, we uncover a connection between the latent space of a variational autoencoder trained using mean-squared-error and the optimal transport distances within the dataset. We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders. Whether using autoencoders or optimal transport distances for anomaly detection, we find that the choices that best represent the background are not necessarily best for signal identification. These challenges with unsupervised anomaly detection bolster the case for additional exploration of semi-supervised or alternative approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Cyber-Physical Events Emulation Based Transmission and Distribution Co-Simulation for Situation Awareness and Grid Anomaly (SAGA) Detection: Preprint

Energy management of transmission and distribution networks is becoming more challenged with the accelerated increasing of distributed energy resources (DERs) such as distributed photovoltaic (PV) generation and distributed energy storage. To better analyze the impacts of DERs on both transmission and distribution systems, a comprehensive transmission and distribution co-simulation platform should be developed. Furthermore, with DERs more actively participated in system operation such as providing real time grid services, their cyber vulnerability should be better understood to maintain system reliability. This paper discussed a cyber-physical events emulation based transmission & distribution co-simulation platform to perform different cyber events emulation and analyze the impacts of cyber physical events happened in distribution system on the T&D system operation. The case studies with both a transmission network and a synthetic distribution network data validate that the proposed T&D co-simulation platform can perform comprehensive cyber physical events emulation. Therefore, with extensive simulation using the proposed model, the system operator can accumulate adequate training data for the system situation awareness and grid anomaly detection purpose.

31 CESER - Office of Cybersecurity, Energy Securit↗

DOE OSTI2026

Economic assessment of seismic monitoring for underground hydrogen storage

Underground hydrogen storage (UHS) plays a key role in the energy landscape. However, like other subsurface engineering technologies, UHS may cause leakage into the groundwater or atmosphere and possibly induce local seismicity. To reduce these risks, seismic monitoring could be a viable technique to track the UHS plume, detect leakages, and locate induced seismicity events. Seismic monitoring has been proposed to safely monitor UHS, but research in this area is still new and requires field studies. Lab and theoretical studies have demonstrated the validity of seismic monitoring for UHS. Therefore, it is imperative to analyze the economic feasibility of seismic monitoring for UHS. Hence, we develop a cost model and open-source Python code for seismic monitoring that considers types of seismometers, comprehensive operational scenarios, detection thresholds, and long-term leakage monitoring. A case study is further provided to validate the cost model on reservoir simulations of UHS. We find that the levelized cost for a 10-year operating UHS site will range on the order of ∼0.003 $\$$/kg. The methods developed in this study could also be applied to the monitoring of groundwater, gas, and/or wastewater injection.

08 HYDROGEN↗

DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Tidal Disruption Event Galaxy Binner

This software simulates astronomical survey detections of tidal disruptions of stars by super-massive black holes. It begins with the synthetic galaxy catalogue described in van Velzen 2008 (https://arxiv.org/abs/1707.03458). The stellar disruption rate in each galaxy is estimated based on Stone & Metzger 2016 (https://arxiv.org/abs/1410.7772). Based on these rates, and the present-day stellar mass function in the galaxy, disruptions are randomly sampled, and the properties of the resulting flares are sampled based on empirical distributions. The code also accounts for obscuration by dust in the host galaxy. Finally, the survey selection effects are applied. The detectable simulated flares are stored in a database, allowing histograms of their properties to be created.

Roth, NathanielJ.↗

DOE OSTI2024

DECAF Cross-device characterization of tokamak disruptions indicated by abnormalities in plasma vertical position and current

Abnormal (deviating from target) variations in the plasma vertical position Z and current I p (such as vertical displacements, transient I p 'spikes' and quenches) constitute common elements of a disruption, a phenomenon that is to be mitigated, or ultimately avoided in future reactor-relevant tokamaks. While those abnormalities are generally recognized cross-shot and cross-device, details in terms of appearance (or not) and order of those abnormalities in disruption event chains is bound with the plasma state at the time of the chain initiation. Detection of those abnormalities is thus indicative not only of the onset of the plasma collapse itself, but also of the disruption driving cause that is promoted at the particular plasma state. Here, occurrence of disruptions, explored via detection of a Ip quench, and analysis of disruption event chains constituted by Ip and Z abnormalities, is reported for in total 7 full years of operation of 3 devices (KSTAR, MAST-U and NSTX-U) using the DECAFTM code expanded tools and capabilities. It is shown that the disruption occurrence depends not only on details of the plasma state, but also on (device-dependent) technical elements of the shot exit scenario. A year-to-year change in main disruption causes and a reduction of the disruptivity rate, bound with device and operation upgrades, are reported. Particular trigger instances of disruption event chains (and the full chains, when applicable) are shown to occupy different parts of the operation space diagrams, in accordance with prior expectation. Plasma elongation is identified as an important factor influencing details of the chains and its role will be further explored.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Implications of the search for optical counterparts during the second part of the Advanced LIGO’s and Advanced Virgo’s third observing run: lessons learned for future follow-up observations

ABSTRACT Joint multimessenger observations with gravitational waves and electromagnetic (EM) data offer new insights into the astrophysical studies of compact objects. The third Advanced LIGO and Advanced Virgo observing run began on 2019 April 1; during the 11 months of observation, there have been 14 compact binary systems candidates for which at least one component is potentially a neutron star. Although intensive follow-up campaigns involving tens of ground and space-based observatories searched for counterparts, no EM counterpart has been detected. Following on a previous study of the first six months of the campaign, we present in this paper the next five months of the campaign from 2019 October to 2020 March. We highlight two neutron star–black hole candidates (S191205ah and S200105ae), two binary neutron star candidates (S191213g and S200213t), and a binary merger with a possible neutron star and a ‘MassGap’ component, S200115j. Assuming that the gravitational-wave (GW) candidates are of astrophysical origin and their location was covered by optical telescopes, we derive possible constraints on the matter ejected during the events based on the non-detection of counterparts. We find that the follow-up observations during the second half of the third observing run did not meet the necessary sensitivity to constrain the source properties of the potential GW candidate. Consequently, we suggest that different strategies have to be used to allow a better usage of the available telescope time. We examine different choices for follow-up surveys to optimize sky localization coverage versus observational depth to understand the likelihood of counterpart detection.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Avoiding thermal runaway in lithium-ion batteries using ultrasound detection of early failure mechanisms

Thermal runaway leading to catastrophic failure has slowed the adoption of lithium-ion batteries, highlighting the need for early warning systems. In this work, ultrasound is used to detect physical changes in 0.950 Ah Li-ion battery cells by inducing conditions associated with thermal runaway. Ultrasound signal features are extracted as the batteries are cycled under baseline conditions and subsequently subjected to constant current and voltage overcharges. Two conditions identifying failure are defined: a warning to detect the start of overcharge and an emergency stop (E-stop), to immediately take the battery out of service. Using changes in ultrasound signal features, consistent warning and stopping classifications are applied to all failure experiments. On average, a warning notification was issued after 15% of the overcharge time and an E-stop was triggered after 35% of total overcharge duration before failure. Further testing suggests that it is possible to avoid thermal runaway by reacting to intermediate, warning notifications prior to an immediate E-stop. In other words, not only can detection derived by ultrasound identify battery failure before a catastrophic event, but can also provide early, actionable warnings so that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.

25 ENERGY STORAGE↗

DOE OSTI2024

Comparison of Sub-Ppm Instrument Response Suggests Higher Detection Limits Could Be Used to Quantify Methane Emissions from Oil and Gas Infrastructure

Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH 4 h –1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.

03 NATURAL GAS↗

DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗

DOE OSTI2023

Infrasound Detections of Low-Magnitude Earthquakes: Preliminary Results of the West Texas Acoustic Experiment

Infrasound observations have grown increasingly important for the monitoring of earthquakes. While large earthquakes generate infrasound that can be detected thousands of kilometers away, there are few near-field observations of infrasound generated by low-magnitude events. We describe preliminary results of the West Texas Acoustic Experiment, during which infrasound sensors collected continuous data in the Permian Basin for a six-month period spanning January—June 2023. During this time, more than 1000 earthquakes with magnitudes between 1.2 and 4.2 occurred within 50 km of the network. We used spectral analysis, array processing, and manual inspection of waveforms to evaluate arrivals of infrasound signals following 84 events with magnitudes between 2.5 and 4.2. Here, we describe eight such events and the infrasound signals associated with each. We find detections of seismic-to-acoustic infrasound signals associated with seven events. We also find strong evidence of a laterally-propagating, purely acoustic wave generated by an M2.9 earthquake.

58 GEOSCIENCES↗

DOE OSTI2021

Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.

Edelen, Jonathan P. (ORCID:0000000215180652)↗

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↗