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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

AN UNEXPECTED DISCOVERY IN THE RICH OPEN CLUSTER NGC 6819 USING XMM-NEWTON

We present the first study of the X-ray population of the intermediate-age rich open cluster NGC 6819 using the XMM-Newton Observatory. In the past decade, Chandra X-ray observations have shown a relationship between the X-ray population of globular clusters and their internal dynamics and encounter frequency. We investigate the role dynamics possibly play in the formation of X-ray sources in NGC 6819, and compare our results with known properties of field and globular cluster X-ray populations. We implement a multi-wavelength approach to studying the X-ray sources, utilizing X-ray and UV data from XMM observations along with the wealth of photometry and radial-velocity data from the WIYN Open Cluster Study and the CFHT Open Cluster Survey. Within the cluster half-light radius, we detect 12 X-ray sources down to a luminosity of 10{sup 30} erg s{sup -1} for cluster members. The sources include a candidate quiescent low-mass X-ray binary (qLMXB), a candidate cataclysmic variable, and two active binary systems. The presence of a qLMXB in an open cluster is previously unexpected given the known relationships between luminous X-ray sources and encounter frequency in globular clusters, and most likely has a dynamical origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Constraints on the normal branch of DGP gravity from SPT galaxy clusters with DES and HST weak-lensing mass calibration and from P l a n c k PR4 CMB anisotropies

We present constraints on the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld gravity model from the abundance of massive galaxy clusters. On scales below the nDGP crossover scale r c , the nDGP model features an effective gravitylike fifth force that alters the growth of structure, leading to an enhancement of the halo mass function (HMF) on cluster scales. The enhanced cluster abundance allows for constraints on the nDGP model using cluster samples. We employ the South Pole Telescope (SPT) cluster sample, selected through the thermal Sunyaev-Zel’dovich effect with the SPT and with mass calibration using weak-lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample contains 1,005 clusters with redshifts 0.25 < z < 1.78 , which are confirmed with the multicomponent matched filter algorithm using optical and near-infrared data. Weak-lensing data from DES and HST enable a robust mass measurement of the cluster sample. We use DES Year 3 data for 688 clusters with redshifts z < 0.95 , and HST data for 39 clusters with redshifts 0.6 < z < 1.7 . We account for the enhancement in the HMF through a semi-analytic correction factor to the standard cosmology HMF derived from the spherical collapse model in the nDGP model. We then further calibrate this model using N -body simulations. In addition, for the first time, we analyze the primary CMB temperature and polarization anisotropy measurements from Planck PR4 within the nDGP model. We obtain a competitive constraint from the joint analysis of the SPT cluster abundance with the Planck PR4 data, and report an upper bound of 1 / H 0 r c < 1.41 at 95% when assuming a cosmology with massive neutrinos.

Vogt, S. M.L. [Munich U. Observ.; LMU Munich (main↗
DOE OSTI2015

Effects of cosmic string velocities and the origin of globular clusters

With the hypothesis that cosmic string loops act as seeds for globular clusters in mind, we study the role that velocities of these strings will play in determining the mass distribution of globular clusters. Loops with high enough velocities will not form compact and roughly spherical objects and can hence not be the seeds for globular clusters. We compute the expected number density and mass function of globular clusters as a function of both the string tension and the peak loop velocity, and compare the results with the observational data on the mass distribution of globular clusters in our Milky Way. We determine the critical peak string loop velocity above which the agreement between the string loop model for the origin of globular clusters (neglecting loop velocities) and observational data is lost.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

Cluster compression algorithm: A joint clustering/data compression concept

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Hilbert, E. E.↗
DOE OSTI2017

Proper Motions and Structural Parameters of the Galactic Globular Cluster M71

By exploiting two ACS/ HST data sets separated by a temporal baseline of ∼7 years, we have determined the relative stellar proper motions (PMs; providing membership) and the absolute PM of the Galactic globular cluster M71. The absolute PM has been used to reconstruct the cluster orbit within a Galactic, three-component, axisymmetric potential. M71 turns out to be in a low-latitude disk-like orbit inside the Galactic disk, further supporting the scenario in which it lost a significant fraction of its initial mass. Since large differential reddening is known to affect this system, we took advantage of near-infrared, ground-based observations to re-determine the cluster center and density profile from direct star counts. The new structural parameters turn out to be significantly different from the ones quoted in the literature. In particular, M71 has a core and a half-mass radii almost 50% larger than previously thought. Finally, we estimate that the initial mass of M71 was likely one order of magnitude larger than its current value, thus helping to solve the discrepancy with the observed number of X-ray sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. Here, the proposed method was tested with 34 traffic congestion cases in the Knoxville, Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline

The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.

Paul Horton↗
DOE OSTI2024

TICC Clustering Library v.1.0

SAND2024-01234O TICC is a clustering algorithm that labels a sequence of data points according to numerical properties. This library is a Python implementation of the algorithm described in "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data" (Hallac et al. 2017). It includes documentation, performance improvements, examples, and test coverage. This library allows users to automatically segment a series of multivariate data points according to their covariance—that is, the way the values at each data point are changing in relation to one another. This is useful for identifying periods in which a system is behaving. For example, if a sensor is measuring a car's velocity, steering wheel angle, braking and acceleration, TICC can determine when the car was stopped, beginning/exiting a turn, slowing or accelerating at an intersection, or driving on straight or curved roads. TICC can be applied to measure multiple quantities at known times. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Dalbey, Keith↗
DOE OSTI2019

Materials data analytics for 9% Cr family steel

A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron–base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first–order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition–based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p–norm distance measures, matching the alloy classification groups adopted by industry. Lastly, the evolutionary method of propagating an ensemble of competing cluster–based models proved to be a viable option in dealing with scarce, multidimensional data.

36 MATERIALS SCIENCE↗
DOE OSTI2013

CONSTRAINING INTERMEDIATE-MASS BLACK HOLES IN GLOBULAR CLUSTERS

Decades after the first predictions of intermediate-mass black holes (IMBHs) in globular clusters (GCs) there is still no unambiguous observational evidence for their existence. The most promising signatures for IMBHs are found in the cores of GCs, where the evidence now comes from the stellar velocity distribution, the surface density profile, and, for very deep observations, the mass-segregation profile near the cluster center. However, interpretation of the data, and, in particular, constraints on central IMBH masses, require the use of detailed cluster dynamical models. Here we present results from Monte Carlo cluster simulations of GCs that harbor IMBHs. As an example of application, we compare velocity dispersion, surface brightness and mass-segregation profiles with observations of the GC M10, and constrain the mass of a possible central IMBH in this cluster. We find that, although M10 does not seem to possess a cuspy surface density profile, the presence of an IMBH with a mass up to 0.75% of the total cluster mass, corresponding to about 600 M{sub Sun }, cannot be excluded. This is also in agreement with the surface brightness profile, although we find it to be less constraining, as it is dominated by the light of giants, causing it to fluctuate significantly. We also find that the mass-segregation profile cannot be used to discriminate between models with and without IMBH. The reason is that M10 is not yet dynamically evolved enough for the quenching of mass segregation to take effect. Finally, detecting a velocity dispersion cusp in clusters with central densities as low as in M10 is extremely challenging, and has to rely on only 20-40 bright stars. It is only when stars with masses down to 0.3 M{sub Sun} are included that the velocity cusp is sampled close enough to the IMBH for a significant increase above the core velocity dispersion to become detectable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Removal of spurious data in Bragg coherent diffraction imaging: an algorithm for automated data preprocessing

Bragg coherent diffraction imaging (BCDI) provides a powerful tool for obtaining high-resolution structural information from nanocrystalline materials. Here a BCDI sample consisting of a large number of randomly oriented nanoscale crystals is considered. Ideally, only one crystal is oriented to produce a Bragg peak on the detector. However, diffraction from other crystals often produces additional signals on the detector. Before the measured diffraction patterns can be processed into structural images, scientists routinely need to manually identify and remove the `alien' intensities from sources other than the intended crystal. With the development of modern high-coherence storage rings, such as the upgraded Advanced Photon Source (APS), the already slow process of manual preprocessing will be untenable for the large volumes of data that will be produced. An automated method of identifying and deleting alien intensities is proposed. This method exploits the fact that BCDI of a perfect crystal produces diffraction data with inversion symmetry around the Bragg peak. This approach uses the machine learning clustering method DBSCAN to distinguish between diffraction from multiple sources, and then calculates cluster size and inversion symmetry to assess whether clusters of intensity belong to desired data or alien signals. This approach can dramatically reduce the amount of time spent manually processing data, allowing BCDI data processing capabilities to keep pace with the technological advances of fourth-generation synchrotron light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Analysis of the Amplitude of the Sunyaev–Zel’dovich Effect out to Redshift z = 0.8

The interaction of the cosmic microwave background (CMB) with the hot gas in clusters of galaxies, the so-called Sunyaev–Zel’dovich (SZ) effect, is a very useful tool that allows us to determine the physical conditions in such clusters and fundamental parameters of the cosmological models. In this work, we determine the dependence of the SZ surface brightness amplitude with redshift and mass of the clusters. We have used PLANCK+IRAS data in the microwave-far-infrared and a catalog with ≳10{sup 5} clusters of galaxies extracted from the SDSS by Wen et al. We estimate and subtract the dust emission from those clusters. From the residual flux, we extract its SZ flux densities. The absolute value of the SZ amplitude indicates that the gas mass is around 10% of the total mass for cluster masses of M∼10{sup 14} M{sub ⊙}. This amplitude is compatible with no evolution with redshift and proportional to M{sup 2.70±0.37} (using X-ray derived masses) or M{sup 2.51±0.38} (using weak-lensing derived masses), with some tension regarding the expectations of the self-similar dependence (amplitude proportional to M{sup 5/3}). Other secondary products of our analysis include that clusters have a dust emission with emissivity index β∼2 and temperature T∼25 K; we confirm that the CMB temperature agrees with a dependence of T{sub 0}(1+z) with clusters of much lower mass than those explored previously; and we find that the cluster masses derived by Wen et al. from a richness-mass relationship are biased by a factor of (1+z){sup −1.8} with respect to the X-ray and weak-lensing measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1975

Further observations of the Orion Nebula cluster

New spectral types for 16 stars, UBVRI photometry for 13 stars and UBVRIHKL photometry for 35 stars in the Orion Nebula cluster are presented. Analysis of these data together with those of Penston (1973) shows that the reddening law in the cluster is normal. Previous claims for high ratios of total to selective absorption are caused by mistaking infrared emission for a hole in the absorption at that wavelength. The distance modulus of M42 is 8.0 + or - 0.1 mag. Star formation in the cluster has been in progress over the last 10 million yr. The infrared colors of cluster stars are correlated with their range of variation in the optical region.

Penston, M. V.↗
NASA NTRS1979

Interactive color display for multispectral imagery using correlation clustering

A method for processing multispectral data is provided, which permits an operator to make parameter level changes during the processing of the data. The system is directed to production of a color classification map on a video display in which a given color represents a localized region in multispectral feature space. Interactive controls permit an operator to alter the size and change the location of these regions, permitting the classification of such region to be changed from a broad to a narrow classification.

Haskell, R. E.↗
NASA NTRS1979

A search for X-ray emission from rich clusters, extended halos around clusters, and superclusters

HEAO A-2 X-ray observations of rich clusters, extended halos around clusters, and superclusters are discussed. Concerning the optically richest clusters, results in the 0.2-60 keV energy range show that they appear to be relatively underluminous in X-rays. It is found that X-ray and microwave measurements for some rich clusters and for Abell 576 can be used to meaningfully constrain intracluster gas parameters and H(0); in addition, observations of Abell 576 show its luminosity to be less than earlier estimates. Extended halos around clusters were not detected in the data, and spectral observations of Abell 401 and 2147 reveal that they have typical cluster spectra with iron-line emission, while intensity measurements indicate that supercluster X-ray emission is not required.

Pravdo, S. H.↗
NASA NTRS2000

[Compton GRO Cycle 7]

The goals of this project were to use the improved location accuracy of combined BATSE/Ulysses-IPN gamma-ray burst (GRB) data to search for small-angle clustering within the burst population and to search for possible correlations between bursts and catalogs of known astrophysical objects. The research was completed in several phases due to the length of time needed to process the basic BATSE/Ulysses data and to develop the appropriate analysis algorithms. In the first phase, BATSE/Ulysses data were gathered from their respective sources and processed to yield, final BATSE burst catalog locations and final BATSE/Ulysses IPN timing annuli. The final data are published. In the next phase, traditional statistical analysis techniques were used to search a subset of the data for small-angle clustering and. correlations, and to produce upper limits. Next, the unique analysis techniques and software described in the proposal were developed and,applied to the final set of data (corresponding to the 4B catalog). The final search did not find any significant self-clustering or correlations with supernovae, AGN, or near by galaxy clusters. The investigation ruled-out earlier findings that indicated (with marginal significance) such results, and. provided stringent limits on the allowable fraction of bursts that are self-clustered and associated with selected known objects. An added benefit of this research came in 1998 when it was suggested that some gamma-ray bursts may be associated with supernova (SN) explosions. We responded rapidly and applied the tools of this investigation to study this possibility. Using combined BATSE/Ulysses data, we were able to completely rule out any association between bright bursts and known supernovae, thereby questioning the validity of any GRB/SN association. If a GRB/SN association exists, it must be only with a small fraction of weak bursts.

Kippen, R. Marc↗
DOE OSTI2023

Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs

DBSCAN is a well-known density-based clustering algorithm to discover arbitrary shape clusters. While conceptually simple in serial, the algorithm is challenging to efficiently parallelize on manycore GPU architectures. Common pitfalls, such as asynchronous range query calls, result in high thread execution divergence in many implementations. In this paper, we propose a new framework for GPU-accelerated DBSCAN, and describe two tree-based algorithms within that framework. Both algorithms fuse the search for neighbors with updating cluster information, but differ in their treatment of dense regions of the data. We show that the time taken to compute clusters is at most twice that of determination of the neighbors. We compare the proposed algorithms with existing CPU and GPU implementations, and demonstrate their competitiveness and performance using a fast traversal structure (bounding volume hierarchy) for low dimensional data. We also show that the memory usage can be reduced by processing object neighbors dynamically without storing them.

Prokopenko, Andrey↗