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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗

NASA NTRS2016

Discovery of a Makemakean Moon

We describe the discovery of a satellite in orbit about the dwarf planet (136472) Makemake. This satellite, provisionally designated S/2015 (136472) 1, was detected in imaging data collected with the Hubble Space Telescope's Wide Field Camera 3 on UTC 2015 April 27 at 7.80 +/- 0.04 mag fainter than Makemake and at a separation of 0farcs57. It likely evaded detection in previous satellite searches due to a nearly edge-on orbital configuration, placing it deep within the glare of Makemake during a substantial fraction of its orbital period. This configuration would place Makemake and its satellite near a mutual event season. Insufficient orbital motion was detected to make a detailed characterization of its orbital properties, prohibiting a measurement of the system mass with the discovery data alone. Preliminary analysis indicates that if the orbit is circular, its orbital period must be longer than 12.4 days and must have a semimajor axis > or approx. = 21,000 km. We find that the properties of Makemake's moon suggest that the majority of the dark material detected in the system by thermal observations may not reside on the surface of Makemake, but may instead be attributable to S/2015 (136472) 1 having a uniform dark surface. This dark moon hypothesis can be directly tested with future James Webb Space Telescope observations. We discuss the implications of this discovery for the spin state, figure, and thermal properties of Makemake and the apparent ubiquity of trans-Neptunian dwarf planet satellites.

Parker, Alex H.↗

DOE OSTI2024

Enhancing the hunt for new phenomena in dijet final states using anomaly detection filters at the high-luminosity large Hadron Collider

In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. Here, to address this, we suggest the application of an anomaly detection approach to eliminate less interesting background events based on event final states. This method not only bypasses the limitations of conventional background models but also significantly enhances our ability to detect potential signals of new physics. Through simulations that mimic the conditions of the upcoming high-luminosity large Hadron collider, we demonstrate the strength and efficiency of this approach in dealing with large data volumes. The integration of unsupervised machine learning into our experimental framework paves the way for a promising avenue to unveil hidden physics discoveries within the overwhelming influx of data.

47 OTHER INSTRUMENTATION↗

NASA NTRS2002

Pathology effects at radiation doses below those causing increased mortality

Mortality data from experiments conducted at the Argonne National Laboratory (ANL) on the long-term effects of external whole-body irradiation on B6CF(1) mice were used to investigate radiation-induced effects at intermediate doses of (60)Co gamma rays or fission-spectrum neutrons either delivered as a single exposure or protracted over 60 once-weekly exposures. Kaplan-Meier analyses were used to identify the lowest dose in the ANL data (within radiation quality, pattern of exposure, and sex) at which radiation-induced mortality caused by primary tumors could be detected (approximately 1-2 Gy for gamma rays and 10-15 cGy for neutrons). Doses at and below these levels were then examined for radiation-induced shifts in the spectrum of pathology detected at death. To do this, specific pathology events were pooled into larger assemblages based on whether they were cancer, cardiovascular disease or non-neoplastic diseases detected within the lungs and pleura, liver and biliary tract, reproductive organs, or urinary tract. Cancer and cardiovascular disease were further subdivided into categories based on whether they caused death, contributed to death, or were simply observed at death. Counts of how often events falling within each of these combined pathology categories occurred within a mouse were then used as predictor variables in logistic regression to determine whether irradiated mice could be distinguished from control mice. Increased pathology burdens were detected in irradiated mice at doses lower than those causing detectable shifts in mortality-22 cGy for gamma rays and 2 cGy for neutrons. These findings suggest that (1) models based on mortality data alone may underestimate radiation effects, (2) radiation may have adverse health consequences (i.e. elevated health risks) even when mortality risks are not detected, and (3) radiation-induced pathologies other than cancer do occur, and they involve multiple organ systems.

Non-NASA Center↗

NASA NTRS2000

Observations of Leonid Meteors Using a Mid-Wave Infrared Imaging Spectrograph

We report broadband 3-5.5 micrometer detections of two Leonid meteors observed during the 1998 Leonid Multi-Instrument Aircraft Campaign. Each meteor was detected at only one position along their trajectory just prior to the point of maximum light emission. We describe the particular aspects of the Aerospace Corp. Mid-wave Infra-Red Imaging Spectrograph (MIRIS) developed for the observation of short duration transient events that impact its ability to detect Leonid meteors. This instrument had its first deployment during the 1998 Leonid MAC. We infer from our observations that the mid-infrared light curves of two Leonid meteors differed from the visible light curve. At the points of detection, the infrared emission in the MIRIS passband was 25 +/- 4 times that at optical wavelengths for both meteors. In addition, we find an upper limit of 800 K for the solid body temperature of the brighter meteor we observed, at the point in the trajectory where we made our mid-wave infrared detection.

Rossano, G. S.↗

NASA NTRS2017

Multiwavelength Follow-up of a Rare Icecube Neutrino Multiplet

IceCube detected three neutrino-induced track events arriving within less than 100s from a similar direction. Expected chance occurrence rate of 1 every 14 years, so not exceptionally rare, but interesting. If astrophysical in nature, the source would have to be relatively nearby or be an exceptional bright neutrino emitter. Follow-up observations by Swift-BAT, Swift-XRT, Master, ASAS-SN, LCOG, Veritas, FACT, and HAWC. The IceCube collaboration wanted to produce a paper summarizing the non-detections and outlining the follow-up network they have assembled. We were asked by Anna Franckowiak to contribute Fermi analysis to their write-up of this event.

Multi wave length↗

NASA NTRS1986

Two classes of solar energetic particle events associated with impulsive and long-duration soft X-ray flares

For the period 1978 September to 1983 December, 67 solar particle events have been identified for which the instruments detected electrons above 3 MeV and for which there are soft X-ray observations. The events are divided into two classes impulsive and long-duration - based on their signature in soft X-rays, and it is found that they have different properties. The events originating with impulsive flares are associated with intensed meter-wavelength type III bursts with associated type V continuum. The events associated with long-duration flares can originate anywhere on the solar disk, extend to much higher proton energies, and are well associated with coronal and interplanetary shocks; for about half of the long-duration events, the associated meter-wavelength events do not include type III bursts. The results discovered by Evenson et al. (1984) and by Kahler et al. (1984) are extended.

Cane, H. V.↗

DOE OSTI2025

Fiber-coupled digital photo sensors for large Time Projection Chambers

Here, this paper presents a novel approach to addressing challenges in neutrino event reconstruction within large Time Projection Chambers (TPCs). By integrating fiber-coupled digital silicon photomultipliers, we propose a design that enhances light detection and improves both energy resolution and event reconstruction. Advancements in power and signal over fiber technologies are leveraged to deploy digital sensors within the TPC bulk volume, enabling precise timing and robust particle identification.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Disentangling Centrality Bias and Final-State Effects in the Production of High-𝑝 𝑇 Neutral Pions Using Direct Photon in 𝑑+Au Collisions at $\sqrt{s_{NN}}$ = 200 GeV

Here, PHENIX presents a simultaneous measurement of the production of direct 𝛾 and 𝜋 0 in 𝑑 + Au collisions at $\sqrt{s_{NN}}$ = 200 GeV over a 𝑝 𝑇 range of 7.5 to 18 GeV/𝑐 for different event samples selected by event activity, i.e., charged-particle multiplicity detected at forward rapidity. Direct-photon yields are used to empirically estimate the contribution of hard-scattering processes in the different event samples. Using this estimate, the average nuclear-modification factor, 𝑅$^{𝜋^0}_{dAu,EXP}$, is 0.925 ± 0.023⁢(stat) ± 0.15⁢(scale), consistent with unity for minimum-bias (MB) 𝑑+Au collisions. For event classes with low and moderate event activity, 𝑅$^{𝜋^0}_{dAu,EXP}$ is consistent with the MB value within 5% uncertainty. This result confirms that the previously observed enhancement of high-𝑝 𝑇 𝜋 0 production found in small-system collisions with low event activity is a result of a bias in interpreting event activity within the Glauber framework. In contrast, for the top 5% of events with the highest event activity, 𝑅$^{𝜋^0}_{dAu,EXP}$ is suppressed by 20% relative to the MB value with a significance of 4.5⁢𝜎, which may be due to final-state effects. This suppression corresponds to a 𝑝 𝑇 shift of 𝛿⁢𝑝 𝑇 = 0.213 ± 0.055 Gev/𝑐 at 9 Gev/𝑐.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗

NASA NTRS2012

Detecting Falling Snow from Space

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms includes determining the thresholds of detection for various active and passive sensor channel configurations, snow event cloud structures and microphysics, snowflake particle electromagnetic properties, and surface types. In this work, cloud resolving model simulations of a lake effect and synoptic snow event were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W -band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) Ku and Ka band, and the GPM Microwave Imager (GMI) channels from 10 to 183 plus or minus 7 GHz. Eleven different snowflake shapes were used to compute radar reflectivities and passive brightness temperatures. Notable results include: (1) the W-Band radar has detection thresholds more than an order of magnitude lower than the future GPM sensors, (2) the cloud structure macrophysics influences the thresholds of detection for passive channels, (3) the snowflake microphysics plays a large role in the detection threshold for active and passive instruments, (4) with reasonable assumptions, "the passive 166 GHz channel has detection threshold values comparable to the GPM DPR Ku and Ka band radars with approximately 0.05 g per cubic meter detected at the surface, or an approximately 0.5-1 millimeter per hr. melted snow rate (equivalent to 0.5-2 centimeters per hr. solid fluffy snowflake rate). With detection levels of falling snow known, we can focus current and future retrieval efforts on detectable storms and concentrate advances on achievable results. We will also have an understanding of the light snowfall events missed by the sensors and not captured in the global estimates.

Gail Skofronick-Jackson↗

DOE OSTI2024

Investigating Gadolinium-Lined Sodium-Iodide Neutron Detectors for Mobile Applications

For enhancing the effectiveness of nonproliferation efforts in neutron detection, most portable instruments rely on 6 Li scintillators, 10 B-based detectors, or gas-filled 3 He proportional counters. Additionally, gamma-ray detectors based on scintillators and semiconductors are often employed for search applications to find radioactive material in the field. These systems typically include dedicated detectors along with separate high voltage supplies and processing electronics for the gamma-ray and neutron detectors. Ideally, a portable radiation detection system should be lightweight, compact, and cost-effective. In the field, scintillators can serve a dual purpose: (1) detecting gamma-rays and (2) detecting neutrons. Gamma-ray detection with scintillators is based on the interaction of gamma-rays within the scintillating material, whereas neutron detection depends indirectly on neutron capture events. These capture events generate conversion electrons and gamma-rays, which can interact with the scintillator. For enhancing neutron capture, the scintillator can be surrounded by neutron absorber materials with a high neutron cross section. The resulting secondary electrons and gamma-rays from neutron interactions, depending on the absorber material used, can then be analyzed to detect the presence of neutron sources. Similarly, semiconductor-based detectors can be employed along with neutron absorbers as liners for neutron detection. 158 Gd has a significantly larger neutron cross section than 3 He, commonly used in gas-filled proportional counters, as shown in Figure 1. For thermal (0.025 eV) neutrons, the absorption cross section of 158 Gd is 10,000 times greater than that of 3 He (refer to Figure 1). This feature makes naturally occurring gadolinium, which consists of 24.8% 158 Gd, a promising neutron absorber material for use in combination with gamma-ray detectors–yielding a hybrid detector–for neutron detection.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2020

Antideuterons and antihelium nuclei from annihilating dark matter

Recent studies of the cosmic-ray antiproton-to-proton ratio have identified an excess of ~10–20 GeV antiprotons relative to the predictions of standard astrophysical models. Intriguingly, the properties of this excess are consistent with the same range of dark matter models that can account for the long-standing excess of γ-rays observed from the Galactic Center. Such dark matter candidates can also produce significant fluxes of antideuterium and antihelium nuclei. In this work we study the production and transport of such particles, both from astrophysical processes as well as from dark matter annihilation. Importantly, in the case of AMS-02, we find that Alfvénic reacceleration (i.e., diffusion in momentum space) can boost the expected number of $\bar {\text d}$ and $^ {\text 3} \overline {\text {He}}$ events from annihilating dark matter by an order of magnitude or more. For relatively large values of the Alfvén speed, and for dark matter candidates that are capable of producing the antiproton and γ-ray excesses, we expect annihilations to produce a few antideuteron events and about one antihelium event in 6 yr of AMS-02 data. This is particularly interesting in light of recent reports from the AMS-02 Collaboration describing the detection of a number of antihelium candidate events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

A Graph Convolutional Network for Active Distribution System Anomaly Detection Considering Measurement Spatial-Temporal Correlations

The accuracy of distribution system state estimation may be significantly impacted by the existence of bad measure-ments and unexpected topology errors. This paper proposes a data-driven Graph Convolutional Network (GCN) for anomaly detection, including bad measurements and topology change events. Compared to many existing machine learning approaches, the proposed approach embeds both spatial-temporal measure-ment correlations, which allows us to detect and distinguish different anomalies. Numerical results carried out on the IEEE 37-node system demonstrate that the proposed-based method can obtain high accuracy in detecting bad data and topology changes as compared to other approaches, even in the presence of high PV penetrations.

active distribution system↗

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↗

NASA NTRS2021

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

Lisa Milani↗

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↗