Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Cluster detection”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

EGRET upper limits to the high-energy gamma-ray emission from the millisecond pulsars in nearby globular clusters

We report upper limits to the high-energy gamma-ray emission from the millisecond pulsars (MSPs) in a number of globular clusters. The observations were done as part of an all-sky survey by the energetic Gamma Ray Experiment Telescope (EGRET) on the Compton Gamma Ray Observatory (CGRO) during Phase I of the CGRO mission (1991 June to 1992 November). Several theoretical models suggest that MSPs may be sources of high-energy gamma radiation emitted either as primary radiation from the pulsar magnetosphere or as secondary radiation generated by conversion into photons of a substantial part of the relativistic e(+/-) pair wind expected to flow from the pulsar. To date, no high-energy emission has been detected from an individual MSP. However, a large number of MSPs are expected in globular cluster cores where the formation rate of accreting binary systems is high. Model predictions of the total number of pulsars range in the hundreds for some clusters. These expectations have been reinforced by recent discoveries of a substantial number of radio MSPs in several clusters; for example, 11 have been found in 47 Tucanae (Manchester et al.). The EGRET observations have been used to obtain upper limits for the efficiency eta of conversion of MSP spin-down power into hard gamma rays. The upper limits are also compared with the gamma-ray fluxes predicted from theoretical models of pulsar wind emission (Tavani). The EGRET limits put significant constraints on either the emission models or the number of pulsars in the globular clusters.

Michelson, P. F.↗
DOE OSTI2021

Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome

Bacteriophages have important roles in the ecology of the human gut microbiome but are under-represented in reference databases. To address this problem, we assembled the Metagenomic Gut Virus catalogue that comprises 189,680 viral genomes from 11,810 publicly available human stool metagenomes. Over 75% of genomes represent double-stranded DNA phages that infect members of the Bacteroidia and Clostridia classes. Based on sequence clustering we identified 54,118 candidate viral species, 92% of which were not found in existing databases. The Metagenomic Gut Virus catalogue improves detection of viruses in stool metagenomes and accounts for nearly 40% of CRISPR spacers found in human gut Bacteria and Archaea. We also produced a catalogue of 459,375 viral protein clusters to explore the functional potential of the gut virome. This revealed tens of thousands of diversity-generating retroelements, which use error-prone reverse transcription to mutate target genes and may be involved in the molecular arms race between phages and their bacterial hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

X-ray nano-imaging of defects in thin film catalysts via cluster analysis

Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, in this study, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO 3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO 3 , and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments.

42 ENGINEERING↗
NASA NTRS2011

Fermi-LAT Detection of Gamma-Ray Emission in the Vicinity of the Star Forming Regions W43 and Westerlund 2

Particle acceleration in massive star forming regions can proceed via a large variety of possible emission scenarios, including high-energy gamma-ray production in the colliding wind zone of the massive Wolf-Rayet binary (here WR 20a and WR I2Ia), collective wind scenarios, diffusive shock acceleration at the boundaries of wind-blown bubbles in the stellar cluster, and outbreak phenomena from hot stellar winds into the interstellar medium. In view of the recent Fermi-LAT detection of HESS JI023-575 (in the vicinity of Westerlund 2), we examine another very high energy (VHE) gamma-ray source, HESS JI848-0145 (in the vicinity ofW43), possibly associated with a massive star cluster. Considering multi-wavelength data, in particular TeV gamma-rays, we examine the available evidence that the gamma-ray emission coincident with Westerlund 2 and W43 could originate in particles accelerated by the above-mentioned mechanisms in massive star clusters.

Lemoine-Goumard, M.↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
NASA NTRS2005

MASS CONTENT AND DYNAMICAL STATE OF 3 OPTICALLY SELECTED HIGH-Z CLUSTERS

As part of our analysis of the Chandra and XMM-Newton observations of the high redshift, optically selected cluster ElS0954-2023/22, we have identified Chandra sources in the field and measured their count rate and fluxes. From the 15 ksec Chandra ACIS-S observation, we detected eleven X-ray sources. Their positions and count rates are listed below. Our analysis also shows faint diffuse emission associated with the optically identified cluster. This information was used in a joint analysis of the XMM-Newton observation of the cluster emission to determine the luminosity and temperature of the diffuse cluster gas.

Mushotzky, Richard F.↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
NASA NTRS1980

A 'cluster' of quasi-stellar objects near M82

The discovery of a cluster of three quasars near the peculiar Irr/Sc galaxy M82 is reported. The three objects were detected by the grism technique to lie in a relatively compact group southeast of M82, with similar redshifts and emission line ratios. Image dissector scanner observations were performed to confirm the nature of the sources as quasars and the quasar redshifts of approximately 2. Consideration of the probability of the chance occurrence of three quasars at this redshift within an angular distance of less than 3.6 arcmin leads to the conclusion that the three quasars are physically associated with each other. If the redshifts of these objects are interpreted as cosmological, then they may make up a cluster of quasars of dimensions comparable to those of galactic clusters. If, on the other hand, the quasars are at the distance of M82, then the noncosmological process which produces their redshifts must be capable of producing them within a narrow range.

Burbidge, E. M.↗
DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Tomographic galaxy clustering with the Subaru Hyper Suprime-Cam first year public data release

We analyze the clustering of galaxies in the first public data release of the HSC Subaru Strategic Program. Despite the relatively small footprints of the observed fields, the data are an excellent proxy for the deep photometric datasets that will be acquired by LSST, and are therefore an ideal test bed for the analysis methods being implemented by the LSST DESC. We select a magnitude limited sample with i<24.5 and analyze it in four redshift bins covering 0.15≲z≲1.5. We carry out a Fourier-space analysis of the two-point clustering of this sample, including all auto- and cross-correlations. We demonstrate the use of map-level deprojection methods to account for fluctuations in the galaxy number density caused by observational systematics. Through an HOD analysis, we place constraints on the characteristic halo masses of this sample, finding a good fit up to scales k max = 1Mpc –1 , including both auto- and cross-correlations. Our results show monotonically decreasing average halo masses, which can be interpreted in terms of the drop-out of red galaxies at high redshifts for a flux-limited sample. In terms of photometric redshift systematics, we show that additional care is needed in order to marginalize over uncertainties in the redshift distribution in galaxy clustering, and that these uncertainties can be constrained by including cross-correlations. We are able to make a ~3σ detection of lensing magnification in the HSC data. Our results are stable to variations in σ 8 and Ω c and we find constraints that agree well with measurements from Planck and low-redshift probes. Lastly, we use our pipeline to study the clustering of galaxies as a function of limiting flux, and provide a simple fitting function for the linear galaxy bias for magnitude limited samples as a function of limiting magnitude and redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Adsorption of Water on JSC-1A Lunar Simulant Samples

Remote sensing probes sent to the moon in the 1990s indicated that water may exist in areas such as the bottoms of deep, permanently shadowed craters at the lunar poles, buried under regolith. Water is of paramount importance for any lunar exploration and colonization project which would require self-sustainable systems. Therefore, investigating the interaction of water with lunar regolith is pertinent to future exploration. The lunar environment can be approximated in ultra-high vacuum systems such as those used in thermal desorption spectroscopy (TDS). Questions about water dissociation, surface wetting, degree of crystallization, details of water-ice transitions, and cluster formation kinetics can be addressed by TDS. Lunar regolith specimens collected during the Apollo missions are still available though precious, so testing with simulant is required before applying to use lunar regolith samples. Hence, we used for these studies JSC-1a, mostly an aluminosilicate glass and basaltic material containing substantial amounts of plagioclase, some olivine and traces of other minerals. Objectives of this project include: 1) Manufacturing samples using as little raw material as possible, allowing the use of surface chemistry and kinetics tools to determine the feasibility of parallel studies on regolith, and 2) Characterizing the adsorption kinetics of water on the regolith simulant. This has implications for the probability of finding water on the moon and, if present, for recovery techniques. For condensed water films, complex TDS data were obtained containing multiple features, which are related to subtle rearrangements of the water adlayer. Results from JSC-1a TDS studies indicate: 1) Water dissociation on JSC-1a at low exposures, with features detected at temperatures as high as 450 K and 2) The formation of 3D water clusters and a rather porous condensed water film. It appears plausible that the sub- m sized particles act as nucleation centers.

Goering, John↗
NASA NTRS1987

Gravitational microlensing and gamma-ray bursts

Models of gravitational microlensing at small and large optical depth are presented, with a special emphasis on the time delays between microimages. The time distribution of microimage arrival times is random when the microobjects responsible for the lensing are randomly distributed. If the microobjects are clustered in space, then microimages are clustered in time. In some models there are many pairs of microimages with a very small time separation. It is proposed that the recently discovered recurrent soft gamma-ray burster GB 790107, for which more than 50 bursts were detected, is a single event multiply imaged due to gravitational microlensing by an ensemble of nonrandomly distributed microobjects, perhaps a rich cluster of galaxies. Some observational tests of this hypothesis are proposed. If correct, the hypothesis requires the burster to be at a cosmological distance, perhaps farther away than the most distant quasars.

Paczynski, Bohdan↗
DOE OSTI2022

Discovery of a Cool, Metal-rich Gas Reservoir in the Outskirts of z ≈ 0.5 Clusters

We built the first-ever statistically significant sample of ≈80,000 background quasar–foreground cluster pairs to study the cool, metal-rich gas in the outskirts (>R 500 ) of z ≈ 0.5 clusters with a median mass of ≈10 14.2 M ⊙ . The sample was obtained by crossmatching the Sloan Digital Sky Survey (SDSS) cluster catalog of Wen & Han and the SDSS quasar catalog of Lyke et al. The median impact parameter (ρcl) of the clusters from the quasar sightlines is 2.4 Mpc (median ρ cl /R 500 = 3.6). A strong Mg II, along with marginal Fe II, absorption is detected in the mean and median stacked spectra of the quasars with a total Mg II rest-frame equivalent width (${W}_{r}^{2796+2803}$) of 0.034 ± 0.005 Å (7σ) and 0.010 ± 0.003 Å (3σ), respectively. The ${W}_{r}^{2796+2803}$ shows a declining trend with increasing ρcl and ρcl/R 500 , but does not show any significant trend with mass (M 500 ) or redshift (z cl ) within the small M500 and z cl ranges probed here. The Mg II absorption signal and the trends persist even if we exclude the quasar–cluster pairs where the background quasars may be probing the circumgalactic medium of bright galaxies with impact parameters <300 kpc. The Mg II (and Fe II) absorption reported here is the first detection of its kind. It indicates the presence of a cool, metal-rich gas reservoir surrounding galaxy clusters out to several R 500 . We suggest that the metal-rich gas in the cluster outskirts arise from stripped materials and that gas stripping may be important out to large clustocentric distances (>3R 500 ).

Intergalactic medium↗
NASA NTRS2001

FUSE Observations of Warm Gas in the Cooling Flow Clusters A1795 and A2597

We present far-ultraviolet spectroscopy of the cores of the massive cooling flow clusters Abell 1795 and 2597 obtained with FUSE. As the intracluster gas cools through 3 x 10(exp 5)K, it should emit strongly in the O VI lambda(lambda)1032,1038 resonance lines. We report the detection of O VI (lambda)1032 emission in A2597, with a line flux of 1.35 +/- 0.35 x 10(exp -15) erg/sq cm s, as well as detection of emission from C III (lambda)977. A marginal detection of C III (lambda)977 emission is also reported for A1795. These observations provide evidence for a direct link between the hot (10(exp 7) K) cooling flow gas and the cool (10(exp 4) K) gas in the optical emission line filaments. Assuming simple cooling flow models, the O VI line flux in A2597 corresponds to a mass deposition rate of approx. 40 solar mass /yr within the central 36 kpc. Emission from O VI (lambda)1032 was not detected in A1795, with an upper limit of 1.5 x 10(exp -15) erg/sq cm s, corresponding to a limit on the mass cooling flow rate of M(28 kpc) less than 28M solar mass/ yr. We have considered several explanations for the lack of detection of O VI emission in A1795 and the weaker than expected flux in A2597, including extinction by dust in the outer cluster, and quenching of thermal conduction by magnetic fields. We conclude that a turbulent mixing model, with some dust extinction, could explain our O VI results while also accounting for the puzzling lack of emission by Fe(sub XVII) in cluster cooling flows.

Oegerle, W. R.↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (Ca x Sr 1–x ) 3 Rh 4 Sn 13 , where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd 2 Re 2 O 7 , to investigate its two much-debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC–revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of 5d 2 Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SPT-SZ MCMF: an extension of the SPT-SZ catalogue over the DES region

We present an extension to a Sunyaev–Zel’dovich Effect (SZE) selected cluster catalogue based on observations from the South Pole Telescope (SPT); this catalogue extends to lower signal to noise than the previous SPT–SZ catalogue and therefore includes lower mass clusters. Optically derived redshifts, centres, richnesses, and morphological parameters together with catalogue contamination and completeness statistics are extracted using the multicomponent matched filter (MCMF) algorithm applied to the S/N > 4 SPT–SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalogue. The main catalogue contains 811 sources above S/N = 4, has 91 per cent purity, and is 95 per cent complete with respect to the original SZE selection. It contains in total 50 per cent more clusters and twice as many clusters above z = 0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalogue completeness. As an example, we provide two subsamples with S/N > 4.25 and S/N > 4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N > 4.5 has 98 per cent purity and 96 per cent completeness and is part of our new combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗