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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Combining resonant and tail-based anomaly detection

In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g., Z / W / h ). This can make these models rich and promising targets for recently developed resonant anomaly detection methods powered by modern machine learning. We demonstrate this using the state-of-the-art classifying anomalies through outer density estimation () method applied to supersymmetry scenarios with gluino pair production. We show that , despite being model agnostic, is nevertheless competitive with dedicated cut-based searches, while simultaneously covering a much wider region of parameter space. The gluino events also populate the tails of the missing energy and H T distributions, making this a novel combination of resonant and tail-based anomaly detection. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
NASA NTRS1994

Measuring temporal gravitational variations using SLR data

Redistribution of mass within the Earth system changes its gravitational field, and thus changes the orbits of Earth satellites. While these variations are small, Satellite Laser Ranging (SLR) to precise geodetic satellites such as Lageos-1, Lageos-2, Starlette, Ajisai, and Stella can detect these changes at their broadest spatial scales (currently greater than 10,000 km). The satellites sense only the combined variation in the solid Earth-ocean-atmosphere system; however, modeling of these different components has led to detection of long-wavelength variations in the distribution of atmospheric mass, changes in the amplitudes of atmospheric and oceanic tides, and secular variations caused by the post-glacial adjustment of the Earth's crust. The unambiguous detection of ocean mass redistribution by SLR has not been verified due largely to inadequacies in current ocean models. Great progress has been made in recent years in the determination of luni-solar tides and the braking they induce in the Earth-Moon-Sun system (leading to secular changes in the length-to-day and lengthening of the lunar orbit period) using a wide variety of techniques including ocean tide gauges, satellite altimetry, Lunar Laser Ranging, and near-Earth satellite orbit modeling. Recent investigations of the more complex and less predictable non-tidal temporal variations in the gravity field have generally proceeded along two fronts: 1) the determination of long-wavelength variations in the gravity field through the changing perturbations seen in the orbits of near-Earth satellites, and 2) the prediction of temporal variations in gravity using geophysical, atmospheric, and oceanic models. A convergence of these efforts is sought to better understand the source of observed changes in the Earth's gravitational field.

Nerem, Robert S.↗
DOE OSTI2020

Implications from Monitoring Gopher Tortoises at Two Spatial Scales

A problem that conservation biologists face is how to monitor species, given both resource limitations and the inherent challenges of assessing long-term demographic processes. We assessed gopher tortoise (Gopherus polyphemus) abundance at a landscape scale and at the scale of 3 local populations within the Conecuh National Forest (CNF).USA between 1991 and 2017. Landscape-level data were collected 26 from line transect distance sampling arranged uniformly across the CNF and collected during a single season (2011); data for local populations were generated from long-term mark-recapture of individuals at three sites selected based on prior knowledge of high density at each. At a landscape scale, we estimated 5,242 (95% CI = 3,538-7,768) tortoises occurred across the approximately 34,000-ha forest, yielding a density of 0.14-0.32 tortoises/ ha. These low densities across the landscape suggest that, on average, management activities across the property have not allowed tortoise populations to retain social structure needed for long-term persistence. The three local populations, however, contained 25-60 individuals and densities of 1.9–6.9 tortoises/ha. Over the study period, populations at two sites were stable and the third experienced significant population growth. Mean annual survival of individuals was 0.89 and invariant across size classes. Altogether, line transect distance sampling is important for assessing landscape-scale abundance of tortoises but may fail to detect local clusters of high-density sites important for population persistence. Our mark-recapture efforts at the local scale revealed that small populations on these high-density sites can exhibit long-term stability or growth even though they do not meet current established criteria for viability. Improved models that incorporate immigration and emigration and better reflect dynamics of peripheral populations would assist in determining how such populations best contribute to species recovery and regional conservation targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside industry R&D labs. In this work, we pair high-performance computing resources including Frontier supercomputer, America's first exascale system, and high-resolution optical RS data to pretrain billion-scale FMs. Our study assesses performance of different pretrained variants of vision Transformers across image classification, semantic segmentation and object detection benchmarks, which highlight the importance of data scaling for effective model scaling. Moreover, we discuss construction of a novel TIU pretraining dataset, model initialization, with data and pretrained models intended for public release. By discussing technical challenges and details often lacking in the related literature, this work is intended to offer best practices to the geospatial community toward efficient training and benchmarking of larger FMs.

Ambrozio Dias, Philipe↗
NASA NTRS2014

The Primordial Inflation Polarization Explorer (PIPER)

The Primordial Inflation Polarization ExploreR (Piper) is a balloon-borne cosmic microwave background (CMB) polarimeter designed to search for evidence of inflation by measuring the large-angular scale CMB polarization signal. Bicep2 recently reported a detection of B-mode power corresponding to the tensor-to-scalar ratio r = 0.2 on approximately 2 degree scales. If the Bicep2 signal is caused by inflationary gravitational waves (IGWs), then there should be a corresponding increase in B-mode power on angular scales larger than 18 degrees. Piper is currently the only suborbital instrument capable of fully testing and extending the Bicep2 results by measuring the B-mode power spectrum on angular scales theta θ = approximately 0.6 deg to 90 deg, covering both the reionization bump and recombination peak, with sensitivity to measure the tensor-to-scalar ratio down to r = 0.007, and four frequency bands to distinguish foregrounds. Piper will accomplish this by mapping 85% of the sky in four frequency bands (200, 270, 350, 600 GHz) over a series of 8 conventional balloon flights from the northern and southern hemispheres. The instrument has background-limited sensitivity provided by fully cryogenic (1.5 K) optics focusing the sky signal onto four 32×40-pixel arrays of time-domain multiplexed Transition-Edge Sensor (TES) bolometers held at 140 milli-Kelvin. Polarization sensitivity and systematic control are provided by front-end Variabledelay Polarization Modulators (VPMs), which rapidly modulate only the polarized sky signal at 3 Hz and allow Piper to instantaneously measure the full Stokes vector (I,Q,U,0V) for each pointing. We describe the Piper instrument and progress towards its first flight.

polarimeter↗
DOE OSTI2014

Situ

Rapidly discovering novel and sophisticated cyber attacks and providing situation aware-ness to analysts are unsolved problems in cyber security. We have developed a platform that scores events in real-time based on probabilistic models to define how typical an event is. This anomaly detection approach is based on unsupervised, probabilistic modeling of data at multiple scales. Scoring events based on multiple scales allows the system to 1) score anomalousness at different levels to detect important events that would otherwise be hidden, and 2) explain to users why an event is anomalous, not just that it is. The system was designed to address several challenges: 1) scaling to very high volume, heterogeneous, streaming data, and 2) minimizing the time from observation to discovery to understanding. The prototype has the real-time framework for pushing scored events to a web-based visualization.

Source record↗
DOE OSTI2021

The H I absorption zoo: JVLA extension to z ~ 0.4

Here, we present an H I 21 cm absorption study of a sample of 26 radio-loud active galactic nuclei (AGN) at 0.25 < z < 0.4 carried out with the Karl G. Jansky Very Large Array. Our aim was to study the rate of incidence of H I in different classes of radio AGN, the morphology and kinematics of the detected H I, and the nature of the interaction between the H I and the radio source at these redshifts. Our sample consists of 14 sources with sizes of up to tens of kpc and 12 compact sources (< a few kpc) in the radio-power range 10 25.7 W Hz –1 –10 26.5 W Hz –1 . We detect H I in five sources, corresponding to a detection rate of ~19%. Within the error bars, this agrees with the detection rate found at lower redshifts. We find that the rest-frame UV luminosities of most of the sources in the sample, including all the detections, are below the proposed threshold above which the H I is supposed to have been ionised. An analysis of the optical emission-line spectra of the sources shows that despite their high radio powers, about one-third of the sample, including two detections, are low-ionisation sources. The radio continuum emission from the sources detected in H I is unresolved at ~5 to 10 kpc scales in our observations, but shows extended structure on parsec scales. We analysed the H I 21 cm absorption spectra of the detections to understand the morphology and kinematics of H I. The absorption profiles are mostly complex with widths between the nulls ranging from ~60 km s –1 to 700 km s –1 . These detections also exhibit remarkably high H I column densities in the range ~10 21 cm –2 –10 22 cm –2 for T spin = 100 K and unit covering factor. Our modelling of the H I 21 cm absorption profiles suggests that in two sources the gas appears to be disturbed, and in three cases, including one with disturbed H I, the majority of the absorption is consistent with it arising from an H I disc. Despite the high radio power of our sources, we do not detect fast outflows. However, the optical emission lines in these detections show the presence of significantly disturbed gas in the nuclear regions in the form of very wide and highly blueshifted emission-line components. Since some of our detections are also low-ionisation AGN, it is quite possible that this disturbance is caused by the radio jets. Overall, our findings point towards a continuation of the low-z trends in the H I detection rate and the incidence of H I in radio AGN up to z ~ 0.4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Magnetic Induction Sensors for Detecting Anomalous Shielding of Radiological and Nuclear Materials: A Feasibility Study

This study examined a concept for improving the nation's ability to detect unauthorized transport of radiological and nuclear material that involves detecting not the radiological or nuclear material itself, but rather the anomalous presence of materials, such as lead and tungsten, typically used to shield such illicit materials from detection. Specifically, the project studied a potentially inexpensive, non-intrusive, and fast method of detecting anomalous shielding: use of magnetic induction sensors to measure currents induced in conductors by a driver coil. The first phase of this work, documented in this report, identified the signatures of materials of interest, assessed the usefulness of measured signatures for discrimination between materials, and then benchmarked models and calculations. The next task involved assessing the strength of these signatures when the materials of interest were enclosed within other materials, such as aluminum and steel, of various thicknesses. A final task mimicked a real-world scenario by testing the ability to detect material signatures of interest at a scale of a vehicle or cargo vessel against a cluttered backdrop, with and without the shielding of another material, such as aluminum or steel. This final task raised doubts regarding whether this detection scheme will be useful under real-world conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2002

Selected mission architectures for the terrestrial planet finder (TPF): large, medium and small

Four team incorporating scientists and engineers from more than 50 universities and 20 engineering firms have assessed techniques for detecting and characterizing terrestrial planets orbiting nearby stars. The primary conclusion from the effort of the past two years is that with suitable technology investment starting now, a mission to detect terrestrial planets around 150 nearby stars could be launched within a decade. Missions of smaller scale could carry out more modest programs capable of detecting and characterizing gas giant planets around tens of stars and of detecting terrestrial planets around the nearest stars.

Terrestrial↗
DOE OSTI2016

Sampling Low-Level Waste - Building a Program - 16101

Low level radioactive waste is routinely sampled and analyzed to demonstrate compliance with Title 10 Code of Federal Regulations Part 61. The sample results are frequently used to create scaling factors to relate the activity of hard to detect (HDT) radionuclides to gamma emitting activity if there is a correlation between the key and scaled radionuclide pair. The activity of gamma emitters can be used with these scaling factors to calculate activity of the low level waste generated in the plant. Another method is to use the sample results directly and scale the total activity to the volume or weight of the waste in the container. Either method assumes that the sample results are representative of the total waste in the container. This requires that the waste streams be accurately defined and that the samples contain radionuclides at ratios that are representative of the bulk waste. This is not always the case and deviations between the sample results and the actual radionuclide concentrations in the waste can lead to miss-classification of the waste resulting in regulatory non-compliance, increased waste disposal costs or both. A deliberate sampling methodology that considers the context of the waste generation and the inherent limitations in practical sampling methodologies will enhance the licensee's ability to demonstrate the analytical results from the sample are representative and defendable. A good sampling program starts with the establishment of waste streams that are consistent with both production mechanisms and plant systems. Once waste streams have been established, data quality objectives can be determined and then the methods for obtaining an appropriate sample can be developed along with the identification of sample points and sample schedules. Sometimes it may not be practical or even possible to obtain a truly representative sample in which case the results from the measurement of the sample must be evaluated within the context of the expected values and the factors inhibiting the collection of a truly representative sample. This paper will explore objectives of a sampling program in relation to the regulations and identify methods that can be used to obtain and evaluate sample data that can be defended as 'representative'. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Incremental k-core decomposition: Algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Theoretical Research at the High Energy Frontier: Cosmology and Beyond

The forefront of particle physics has focused on possible physics beyond the standard model which might help explain its peculiarities, including the nature of the spectrum of masses of elementary particles, the peculiar hierarchy between the Planck scale and the electroweak scale, and the possible manner in which the standard model might be embedded in a quantum theory which incorporates gravity. Over the past several decades it has become clear that several of the key out-standing problems associated with our understanding of fundamental interactions are inextricably tied to questions that are also of current interest in cosmology and astrophysics. At the same time, remarkable new data is being gathered that will allow empirical testing of theoretical ideas that have been around for a generation, from the discovery of the Higgs at the LHC to the possible detection of gravitational waves from Inflation at the GUT scale. The questions of the origin of mass, and possible grand unification are both tied to the possible existence of phase transitions in the early universe. Neutrino masses, as probed from astrophysical sources, may play a key role in elucidating the physics associated with the generation of baryon number. It is also possible that new physics at the electroweak scale may play a role in the nature of primordial cosmological magnetic fields. Low Energy Supersymmetry as a solution to the hierarchy problem can predict, besides events detectable at the LHC, stable weakly interacting particles that might make up the dark matter of the universe. The possible existence of large extra dimensions might also impact upon the hierarchy problem, but these could also dramatically affect our picture of the evolution of the Universe both at early times, and possibly on large scales. Inflation may depend upon new physics at the GUT scale, but its detection may now be imminent with the possible detection of a gravitational wave signature in the Cosmic Microwave Background Radiation. Undoubtedly the most significant outstanding problem in high-energy physics is also a problem in cosmology, and indeed originated not from accelerators but from astrophysical observations: What is the origin and nature of the dark energy that appears to dominate the Universe? An understanding of quantum gravity, and perhaps a new understanding of quantum mechanics or quantum field theory may be required to fully address this problem. At the moment, the physics of black holes may provide the best opportunity to explore these issues, while the discovery of the Higgs suggests several new possible connections to physics that might be relevant for dark energy. Finally, pending confirmation of a gravitational wave signal from inflation, to date the only direct evidence for fundamental particle physics beyond the standard model comes, at least in part, from astrophysical neutrino observations. A remarkable convergence of theory, observation and experiment has been taking place that is allowing great strides to be made in our knowledge of the parameters that describe the universe, if not the origin of these parameters. Given the new discoveries now being made, and the incredible capabilities of future instruments, it is an exciting time to make progress in our fundamental understanding the origin and evolution of the Universe and the fundamental forces that guide that evolution. As a result, it is natural that our DOE theory research program at Arizona State University focuses in large part on the connections between particle physics and cosmology and astrophysics in order to improve our understanding of fundamental physics. Our areas of research cover all of the areas described above. Our group now consists of four faculty PI’s and their postdocs and students, complemented by long term visitor Frank Wilczek, and physics faculty colleagues Cecilia Lunardini, Richard Lebed, and Andrei Belitsky, whose interests overlap in areas ranging from particle theory and phenomenology to neutrino astrophysics. In addition, we interact with astronomers, and experimentalists in both Physics and the School of Earth and Space Exploration. In addition, Krauss and Parikh are associated, respectively, with the ASU Origins Project and the ASU Beyond Center. Both of these groups have helped us leverage DOE funds by supporting workshops associated with our activities from time to time. To continue the active program we have built up here, we are asking for support for 3 graduate students, and 3 postdocs (note that the PI will forego summer salary support in order to support one additional postdoc beyond the request in our last proposal for 2 postdocs). We have been fortunate to build a vibrant group based in part on University startup support for our program. Now that that support is coming to a close for most of our group, we are hoping that the exciting program we have created motivates continued DOE support at a level that, while not as great as the level we enjoyed with startup support, will nevertheless allow us to maintain our momentum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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 OSTI2013

Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain

Purpose: The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain.Methods: Two cylindrical rods (3-mm and 5-mm diameters) were placed in a 35 × 26 cm torso-shaped water phantom to simulate lesions with −15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128-slice CT scanner at each of four dose levels (CTDIvol = 5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate images with signal-present, with each ROI containing 128 × 128 pixels. Corresponding ROIs of signal-absent images were generated from images without lesion mimicking rods. The location of the lesion (rod) in each ROI was randomly distributed by moving the ROIs around each lesion. Human observer studies were performed by having three trained observers identify the presence or absence of lesions, indicating the lesion location in each image and scoring confidence for the detection task on a 6-point scale. The same image data were analyzed using a channelized Hotelling model observer (CHO) with Gabor channels. Internal noise was added to the decision variables for the model observer study. Area under the curve (AUC) of ROC and localization ROC (LROC) curves were calculated using a nonparametric approach. The Spearman's rank order correlation between the average performance of the human observers and the model observer performance was calculated for the AUC of both ROC and LROC curves for both the 3- and 5-mm diameter lesions.Results: In both ROC and LROC analyses, AUC values for the model observer agreed well with the average values across the three human observers. The Spearman's rank order correlation values for both ROC and LROC analyses for both the 3- and 5-mm diameter lesions were all 1.0, indicating perfect rank ordering agreement of the figures of merit (AUC) between the average performance of the human observers and the model observer performance.Conclusions: In CT imaging of different sizes of low-contrast lesions (−15 HU), the performance of CHO with Gabor channels was highly correlated with human observer performance for the detection and localization tasks with uncertain lesion location in CT imaging at four clinically relevant dose levels. This suggests the ability of Gabor CHO model observers to meaningfully assess CT image quality for the purpose of optimizing scan protocols and radiation dose levels in detection and localization tasks for low-contrast lesions.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

CONGO²: Scalable Online Anomaly Detection and Localization in Power Electronics Networks

Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. Here in this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors’ inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for infield deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

42 ENGINEERING↗
DOE OSTI2023

Joint Detection and Communication over Type-Sensitive Networks

Due to the difficulty of decentralized inference with conditional dependent observations, and motivated by large-scale heterogeneous networks, we formulate a framework for decentralized detection with coupled observations. Each agent has a state, and the empirical distribution of all agents’ states or the type of network dictates the individual agents’ behavior. In particular, agents’ observations depend on both the underlying hypothesis as well as the empirical distribution of the agents’ states. Hence, our framework captures a high degree of coupling, in that an individual agent’s behavior depends on both the underlying hypothesis and the behavior of all other agents in the network. Considering this framework, the method of types, and a series of equicontinuity arguments, we derive the error exponent for the case in which all agents are identical and show that this error exponent depends on only a single empirical distribution. The analysis is extended to the multi-class case, and numerical results with state-dependent agent signaling and state-dependent channels highlight the utility of the proposed framework for analysis of highly coupled environments.

42 ENGINEERING↗
DOE OSTI2021

SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.

Wang, Tiantong↗