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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

The metallicity of the intracluster medium over cosmic time: further evidence for early enrichment

Here, we use Chandra X-ray data to measure the metallicity of the intracluster medium (ICM) in 245 massive galaxy clusters selected from X-ray and Sunyaev–Zel'dovich (SZ) effect surveys, spanning redshifts 0 < z < 1.2. Metallicities were measured in three different radial ranges, spanning cluster cores through their outskirts. We explore trends in these measurements as a function of cluster redshift, temperature and surface brightness ‘peakiness’ (a proxy for gas cooling efficiency in cluster centres). The data at large radii (0.5–1 r500) are consistent with a constant metallicity, while at intermediate radii (0.1–0.5 r500) we see a late-time increase in enrichment, consistent with the expected production and mixing of metals in cluster cores. In cluster centres, there are strong trends of metallicity with temperature and peakiness, reflecting enhanced metal production in the lowest entropy gas. Within the cool-core/sharply peaked cluster population, there is a large intrinsic scatter in central metallicity and no overall evolution, indicating significant astrophysical variations in the efficiency of enrichment. The central metallicity in clusters with flat surface brightness profiles is lower, with a smaller intrinsic scatter, but increases towards lower redshifts. Our results are consistent with other recent measurements of ICM metallicity as a function of redshift. They reinforce the picture implied by observations of uniform metal distributions in the outskirts of nearby clusters, in which most of the enrichment of the ICM takes place before cluster formation, with significant later enrichment taking place only in cluster centres, as the stellar populations of the central galaxies evolve.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Determination of hydrogen cluster velocities and comparison with numerical calculations

The use of powerful hydrogen cluster jet targets in storage ring experiments led to the need of precise data on the mean cluster velocity as function of the stagnation temperature and pressure for the determination of the volume density of the target beams. For this purpose a large data set of hydrogen cluster velocity distributions and mean velocities was measured at a high density hydrogen cluster jet target using a trumpet shaped nozzle. The measurements have been performed at pressures above and below the critical pressure and for a broad range of temperatures relevant for target operation, e.g., at storage ring experiments. The used experimental method is described which allows for the velocity measurement of single clusters using a time-of-flight technique. Since this method is rather time-consuming and these measurements are typically interfering negatively with storage ring experiments, a method for a precise calculation of these mean velocities was needed. For this, the determined mean cluster velocities are compared with model calculations based on an isentropic one-dimensional van der Waals gas. Based on the obtained data and the presented numerical calculations, a new method has been developed which allows to predict the mean cluster velocities with an accuracy of about 5%. For this two cut-off parameters defining positions inside the nozzle are introduced, which can be determined for a given nozzle by only two velocity measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Proper Motions Study of the Globular Cluster NGC 3201

With a high value of heliocentric radial velocity, a retrograde orbit, and suspected to have an extragalactic origin, NGC 3201 is an interesting globular cluster for kinematical studies. Our purpose is to calculate the relative proper motions (PMs) and membership probability for the stars in the wide region of globular cluster NGC 3201. PM based membership probabilities are used to isolate the cluster sample from the field stars. The membership catalog will help address the question of chemical inhomogeneity in the cluster. Archive CCD data taken with a wide-field imager (WFI) mounted on the ESO 2.2 m telescope are reduced using the high-precision astrometric software developed by Anderson et al. for the WFI images. The epoch gap between the two observational runs is ∼14.3 years. To standardize the BVI photometry, Stetson’s secondary standard stars are used. The CCD data with an epoch gap of ∼14.3 years enables us to decontaminate the cluster stars from field stars efficiently. The median precision of PMs is better than ∼0.8 mas yr{sup −1} for stars having V<18 mag that increases up to ∼1.5 mas yr{sup −1} for stars with 18<V<20 mag. Kinematic membership probabilities are calculated using PMs for stars brighter than V∼20 mag. An electronic catalog of positions, relative PMs, BVI magnitudes, and membership probabilities in the ∼19.7 × 17 arcmin{sup 2} region of NGC 3201 is presented. We use our membership catalog to identify probable cluster members among the known variables and X-ray sources in the direction of NGC 3201.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Iron, magnesium, and silicon in dust from Comet Halley

The highest-quality impact mass spectrometer data from the Vega-1 and Giotto spacecraft are presently used to study the Mg, Si, and Fe composition of dust grains in Comet Halley. The results thus obtained are in general agreement with previously reported data, but differ with respect to ion ratio dispersions. A lack of sharp clustering in the data indicates that none of the detected particles can be characterized as single mineral grains; an abundant glass content in the solids may be indicated. The best match of the distribution of Fe/(Fe+Mg) is with interplanetary particles containing high temperature, Mg-rich silicates dominated by anhydrous minerals, so that Comet Halley may be a mixture of ice and high-temperature anhydrous minerals.

Lawler, Mark E.↗
NASA NTRS1993

Morphology and kinematics in clusters of galaxies

The existence of subclustering, infall, and the morphology-local density relation suggest that there should be differences in the kinematics of different morphological populations in clusters. From published data, we compile a sample of galaxy redshifts and types in six rich clusters of galaxies: A548, A1060, A1644, A1656, A2l51, and DC 2048 - 52. In three clusters, the velocity distribution of spiral galaxies is different from those of other morphological populations: (1) spirals and ellipticals differ in A1656 and A2151, and (2) spirals and S0s differ in DC 2048 - 52. The differences in the distributions result primarily from differences in the mean velocities. The results are consistent with a picture in which a cluster irregularly accretes clumps of mostly spiral galaxies from the field, causing the combined velocity distribution to be asymmetric. These clumps might infall within the plane of a wall like the Great Wall of galaxies. Recent dynamical simulations show that the asymmetries may persist until after the clumps have merged with the central mass concentration.

Zabludoff, Ann I.↗
DOE OSTI2016

Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to 1.6TB particle physics, 2.2TB and 16TB climate modeling and 1.1TB bioimaging data. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark’s data parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance.

Gittens, Alex↗
DOE OSTI2025

The final WaZP galaxy cluster catalog of the Dark Energy Survey and comparison with SZE data

In this work, we present and characterize the galaxy cluster catalog detected by the WaZP cluster finder, which is not based on red-sequence identification, on the full six years of observations of the Dark Energy Survey (DES-Y6). The full catalog contains over 400k detected clusters with richnesses, Ngals, above 5 and that reach redshifts up to 1.3. We also provide a version of the catalog where the observation depth and richness computation are homogenized to be used for cosmology, containing 33k rich (Ngals >25) clusters. We compare our results with the previous WaZP catalog obtained from the DES first-year data release (DES-Y1). We find that essentially all clusters within the common footprint and depth limit are recovered. The deeper observations on DES-Y6 and the more complete available spectroscopic redshift sample lead to improvements in the redshifts of the clusters, resulting in an average scatter of 1.4% and offset of 0.2%. The optical clusters are also cross-matched with Sunyaev Zel'dovich Effect (SZE) cluster samples detected by the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). We find that essentially all SZE clusters with reasonable overlapping footprint have a corresponding WaZP cluster. Conversely, 90% of the optical detections with richness greater than 150 have a counterpart in the deeper regions of the SZE surveys. Based on cross-match with the SZE catalogs, we also find that 15-20% of the SZE matched systems have more than one possible WaZP counterpart at the same redshift and within the SZE R500c, indicating possible interacting or unrelaxed systems. Finally, given the optical and SZE beams, WaZP and SZE centerings are found to be consistent. A more detailed study of the SZE-WaZP mass-richness relation will be presented in a separate paper.

Benoist, C. [OCA, Nice, Lab. Lagrange; LIneA, Rio ↗
DOE OSTI2016

SPT-GMOS: A GEMINI/GMOS-SOUTH SPECTROSCOPIC SURVEY OF GALAXY CLUSTERS IN THE SPT-SZ SURVEY

We present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg{sup 2} of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goal of these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W , of [O ii] λλ 3727, 3729 and H- δ , and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, star-forming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m {sup ⋆}). Finally, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ∼20% of the full SPT-SZ sample.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
NASA NTRS1987

X-ray selected high redshift clusters of galaxies

Studies of the evolution of X-ray emission from clusters of galaxies have so far used distant clusters discovered optically and then observed at X-ray wavelengths (Henry et al., 1982; Henry and Lavery, 1984). A different approach to the study of cluster evolution is to use clusters selected directly by their X-ray emission, since X-ray selection is extremely successful at discovering high-redshift clusters (up to z = 0.5). The Extension of the Medium Sensitivity Survey, a study of serendipitous X-ray sources discovered with the Einstein Imaging Proportional Counter, has so far provided a sample of more than 60 clusters with relatively high redshifts. Upon completion of the identification programs the sample will include over 100 clusters. Here the data currently available are presented, and the basic properties of this new sample of clusters of galaxies are discussed. A preliminary number-counts distribution is also derived.

Gioia, Isabella M.↗
NASA NTRS2018

In Situ Measurements of Angular-Dependent Light Scattering by Aerosols over the Contiguous United States

This work provides a synopsis of aerosol phase function (F (sub 11)) and polarized phase function (F (sub 12)) measurements made by the Polarized Imaging Nephelometer (PINeph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F (sub 11) and minus F (sub 12) divided by F (sub 11) for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 percent recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.

Aerosol phase function↗
DOE OSTI2020

SPIDERS: overview of the X-ray galaxy cluster follow-up and the final spectroscopic data release

ABSTRACT SPIDERS (The SPectroscopic IDentification of eROSITA Sources) is a large spectroscopic programme for X-ray selected galaxy clusters as part of the Sloan Digital Sky Survey-IV (SDSS-IV). We describe the final data set in the context of SDSS Data Release 16 (DR16): the survey overall characteristics, final targeting strategies, achieved completeness, and spectral quality, with special emphasis on its use as a galaxy cluster sample for cosmology applications. SPIDERS now consists of about 27 000 new optical spectra of galaxies selected within 4000 photometric red sequences, each associated with an X-ray source. The excellent spectrograph efficiency and a robust analysis pipeline yield a spectroscopic redshift measurement success rate exceeding 98 per cent, with a median velocity accuracy of 20 km s−1 (at z = 0.2). Using the catalogue of 2740 X-ray galaxy clusters confirmed with DR16 spectroscopy, we reveal the 3D map of the galaxy cluster distribution in the observable Universe up to z ∼ 0.6. We highlight the homogeneity of the member galaxy spectra among distinct regions of the galaxy cluster phase space. Aided by accurate spectroscopic redshifts and by a model of the sample selection effects, we compute the galaxy cluster X-ray luminosity function and we present its lack of evolution up to z = 0.6. Finally we discuss the prospects of forthcoming large multiplexed spectroscopic programmes dedicated to follow up the next generation of all-sky X-ray source catalogues.

Clerc, N.↗
NASA NTRS2005

Testing the Archivas Cluster (Arc) for Ozone Monitoring Instrument (OMI) Scientific Data Storage

The Ozone Monitoring Instrument (OMI) launched on NASA's Aura Spacecraft, the third of the major platforms of the EOS program on July 15,2004. In addition to the long term archive and distribution of the data from OM1 through the Goddard Earth Science Distributed Active Archive Center (GESDAAC), we are evaluating other archive mechanisms that can archive the data in a more immediately available method where it can be used for futher data production and analysis. In 2004, Archivas, Inc. was selected by NASA s Small Business Innovative Research (SBIR) program for the development of their Archivas Cluster (ArC) product. Arc is an online disk based system utilizing self-management and automation on a Linux cluster. Its goal is to produce a low cost solution coupled with the ease of management. The OM1 project is an application partner of the SBIR program, and has deployed a small cluster (5TB) based on the beta Archwas software. We performed extensive testing of the unit using production OM1 data since launch. In 2005, Archivas, Inc. was funded in SBIR Phase II for further development, which will include testing scalability with the deployment of a larger (35TB) cluster at Goddard. We plan to include Arc in the OM1 Team Leader Computing Facility (TLCF) hosting OM1 data for direct access and analysis by the OMI Science Team. This presentation will include a brief technical description of the Archivas Cluster, a summary of the SBIR Phase I beta testing results, and an overview of the OMI ground data processing architecture including its interaction with the Phase II Archivas Cluster and hosting of OMI data for the scientists.

Tilmes, Curt↗
DOE OSTI2018

Two-level Clustering-based Target Detection Through Sensor Deployment and Data Fusion

Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Rao, Nageswara↗
NASA NTRS1982

Optimization of a Non-traditional Unsupervised Classification Approach for Land Cover Analysis

The conditions under which a hybrid of clustering and canonical analysis for image classification produce optimum results were analyzed. The approach involves generation of classes by clustering for input to canonical analysis. The importance of the number of clusters input and the effect of other parameters of the clustering algorithm (ISOCLS) were examined. The approach derives its final result by clustering the canonically transformed data. Therefore the importance of number of clusters requested in this final stage was also examined. The effect of these variables were studied in terms of the average separability (as measured by transformed divergence) of the final clusters, the transformation matrices resulting from different numbers of input classes, and the accuracy of the final classifications. The research was performed with LANDSAT MSS data over the Hazleton/Berwick Pennsylvania area. Final classifications were compared pixel by pixel with an existing geographic information system to provide an indication of their accuracy.

Boyd, R. K.↗
DOE OSTI2014

NuSTAR observations of the bullet cluster: constraints on inverse Compton emission

The search for diffuse non-thermal inverse Compton (IC) emission from galaxy clusters at hard X-ray energies has been undertaken with many instruments, with most detections being either of low significance or controversial. Because all prior telescopes sensitive at E > 10 keV do not focus light and have degree-scale fields of view, their backgrounds are both high and difficult to characterize. The associated uncertainties result in lower sensitivity to IC emission and a greater chance of false detection. In this work, we present 266 ks NuSTAR observations of the Bullet cluster, which is detected in the energy range 3-30 keV. NuSTAR's unprecedented hard X-ray focusing capability largely eliminates confusion between diffuse IC and point sources; however, at the highest energies, the background still dominates and must be well understood. To this end, we have developed a complete background model constructed of physically inspired components constrained by extragalactic survey field observations, the specific parameters of which are derived locally from data in non-source regions of target observations. Applying the background model to the Bullet cluster data, we find that the spectrum is well—but not perfectly—described as an isothermal plasma with kT = 14.2 ± 0.2 keV. To slightly improve the fit, a second temperature component is added, which appears to account for lower temperature emission from the cool core, pushing the primary component to kT ∼ 15.3 keV. We see no convincing need to invoke an IC component to describe the spectrum of the Bullet cluster, and instead argue that it is dominated at all energies by emission from purely thermal gas. The conservatively derived 90% upper limit on the IC flux of 1.1 × 10{sup –12} erg s{sup –1} cm{sup –2} (50-100 keV), implying a lower limit on B ≳ 0.2 μG, is barely consistent with detected fluxes previously reported. In addition to discussing the possible origin of this discrepancy, we remark on the potential implications of this analysis for the prospects for detecting IC in galaxy clusters in the future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Planck 2015 results: XXIII. The thermal Sunyaev-Zeldovich effect-cosmic infrared background correlation

In this paper, we use Planck data to detect the cross-correlation between the thermal Sunyaev-Zeldovich (tSZ) effect and the infrared emission from the galaxies that make up the the cosmic infrared background (CIB). We first perform a stacking analysis towards Planck-confirmed galaxy clusters. We detect infrared emission produced by dusty galaxies inside these clusters and demonstrate that the infrared emission is about 50% more extended than the tSZ effect. Modelling the emission with a Navarro-Frenk-White profile, we find that the radial profile concentration parameter is c 500 = 1.00 +0.18 -0.15 . This indicates that infrared galaxies in the outskirts of clusters have higher infrared flux than cluster-core galaxies. We also study the cross-correlation between tSZ and CIB anisotropies, following three alternative approaches based on power spectrum analyses: (i) using a catalogue of confirmed clusters detected in Planck data; (ii) using an all-sky tSZ map built from Planck frequency maps; and (iii) using cross-spectra between Planck frequency maps. With the three different methods, we detect the tSZ-CIB cross-power spectrum at significance levels of (i) 6σ; (ii) 3σ; and (iii) 4σ. We model the tSZ-CIB cross-correlation signature and compare predictions with the measurements. The amplitude of the cross-correlation relative to the fiducial model is A tSZ-CIB = 1.2 ± 0.3. Finally, this result is consistent with predictions for the tSZ-CIB cross-correlation assuming the best-fit cosmological model from Planck 2015 results along with the tSZ and CIB scaling relations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Web-based wide-area monitoring platform for ringdown and clustering analytics in power systems

This paper introduces an open-source research platform for monitoring the Mexican interconnected power grid, allowing real-time processing and information extraction of the grid’s dynamic condition. Moreover, the platform is a Python-based development that embeds different ringdown and clustering analytics tools. In the case of ringdown analysis, the modal information can be extracted using some of the most known algorithms, i.e., Prony analysis, eigensystem realization algorithm (ERA), and matrix pencil (MP). For clustering analysis, the coherent behaviour of generator and non-generator buses is provided by applying recent state-of-the-art techniques such as affinity propagation, K-means, hierarchical agglomerative clustering, and typicality data analysis. The results of up to 93 PMUs show that this open-source platform suits researchers’ and engineers’ power system dynamic analysis requirements.

Clustering↗