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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Age determination of 15 old to intermediate-age small Magellanic cloud star clusters

We present color-magnitude diagrams in the V and I bands for 15 star clusters in the Small Magellanic Cloud (SMC) based on data taken with the Very Large Telescope (VLT, Chile). We selected these clusters from our previous work, wherein we derived cluster radial velocities and metallicities from calcium II infrared triplet (CaT) spectra also taken with the VLT. We discovered that the ages of six of our clusters have been appreciably underestimated by previous studies, which used comparatively small telescopes, graphically illustrating the need for large apertures to obtain reliable ages of old and intermediate-age SMC star clusters. In particular, three of these clusters, L4, L6, and L110, turn out to be among the oldest SMC clusters known, with ages of 7.9 ± 1.1, 8.7 ± 1.2, and 7.6 ± 1.0 Gyr, respectively, helping to fill a possible 'SMC cluster age gap'. Using the current ages and metallicities from Parisi et al., we analyze the age distribution, age gradient, and age-metallicity relation (AMR) of a sample of SMC clusters measured homogeneously. There is a suggestion of bimodality in the age distribution but it does not show a constant slope for the first 4 Gyr, and we find no evidence for an age gradient. Due to the improved ages of our cluster sample, we find that our AMR is now better represented in the intermediate/old period than we had derived in Parisi et al., where we simply took ages available in the literature. Additionally, clusters younger than ∼4 Gyr now show better agreement with the bursting model of Pagel and Tautvaišienė, but we confirm that this model is not a good representation of the AMR during the intermediate/old period. A more complicated model is needed to explain the SMC chemical evolution in that period.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Hα star formation rates of z > 1 galaxy clusters in the IRAC shallow cluster survey

We present Hubble Space Telescope near-IR spectroscopy for 18 galaxy clusters at 1.0 <z < 1.5 in the IRAC Shallow Cluster Survey. We use Wide Field Camera 3 grism data to spectroscopically identify Hα emitters in both the cores of galaxy clusters as well as in field galaxies. We find a large cluster-to-cluster scatter in the star formation rates within a projected radius of 500 kpc, and many of our clusters (∼60%) have significant levels of star formation within a projected radius of 200 kpc. A stacking analysis reveals that dust reddening in these star-forming galaxies is positively correlated with stellar mass and may be higher in the field than the cluster at a fixed stellar mass. This may indicate a lower amount of gas in star-forming cluster galaxies than in the field population. Also, Hα equivalent widths of star-forming galaxies in the cluster environment are still suppressed below the level of the field. This suppression is most significant for lower mass galaxies (log M {sub *} < 10.0 M {sub ☉}). We therefore conclude that environmental effects are still important at 1.0 <z < 1.5 for star-forming galaxies in galaxy clusters with log M {sub *} ≲ 10.0 M {sub ☉}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

MeshAndCollect: an automated multi-crystal data-collection workflow for synchrotron macromolecular crystallography beamlines

The fully automated collection and merging of partial data sets from a series of cryocooled crystals of biological macromolecules contained on the same support is presented, as are the results of test experiments carried out on various systems. Here, an automated procedure is described to identify the positions of many cryocooled crystals mounted on the same sample holder, to rapidly predict and rank their relative diffraction strengths and to collect partial X-ray diffraction data sets from as many of the crystals as desired. Subsequent hierarchical cluster analysis then allows the best combination of partial data sets, optimizing the quality of the final data set obtained. The results of applying the method developed to various systems and scenarios including the compilation of a complete data set from tiny crystals of the membrane protein bacteriorhodopsin and the collection of data sets for successful structure determination using the single-wavelength anomalous dispersion technique are also presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1988

Integrated-light spectroscopy of globular clusters at the infrared Ca II lines

Integrated-light spectroscopy has been obtained for 27 globular clusters at the Ca II IR triplet. Line strengths and radial velocities have been measured from the spectra. For the well-studied clusters in the sample, the strength of the Ca II lines is very well correlated with previous metallicity estimates. Thus, the triplet is useful as a metallicity indicator in globular cluster integrated-light spectra. The greatly reduced effect of interstellar extinction at these wavelengths (compared to the blue region of the spectrum) has permitted observations of some of the most heavily reddened clusters in the Galaxy. For several such clusters, the Ca II triplet metallicities are in poor agreement with metallicity estimates from IR photometry by Malkan (1981). The strength of an interstellar band at 8621A has been used to estimate the amount of extinction towards these clusters. Using the new metallicity and radial-velocity data, the metallicity distribution, kinematics, and spatial distribution of the disk globular cluster system have been analyzed. Results very similar to those of Zinn (1985) have been found. The relation of the disk globulars to the stellar thick disk is discussed.

Armandroff, Taft E.↗
NASA NTRS1994

Unsupervised Segmentation Of Polarimetric SAR Data

Method of unsupervised segmentation of polarimetric synthetic-aperture-radar (SAR) image data into classes involves selection of classes on basis of multidimensional fuzzy clustering of logarithms of parameters of polarimetric covariance matrix. Data in each class represent parts of image wherein polarimetric SAR backscattering characteristics of terrain regarded as homogeneous. Desirable to have each class represent type of terrain, sea ice, or ocean surface distinguishable from other types via backscattering characteristics. Unsupervised classification does not require training areas, is nearly automated computerized process, and provides nonsubjective selection of image classes naturally well separated by radar.

Rignot, Eric J.↗
NASA NTRS2003

Spatial Analysis of Great Lakes Regional Icing Cloud Liquid Water Content

Abstract Clustering of cloud microphysical conditions, such as liquid water content (LWC) and drop size, can affect the rate and shape of ice accretion and the airworthiness of aircraft. Clustering may also degrade the accuracy of cloud LWC measurements from radars and microwave radiometers being developed by the government for remotely mapping icing conditions ahead of aircraft in flight. This paper evaluates spatial clustering of LWC in icing clouds using measurements collected during NASA research flights in the Great Lakes region. We used graphical and analytical approaches to describe clustering. The analytical approach involves determining the average size of clusters and computing a clustering intensity parameter. We analyzed flight data composed of 1-s-frequency LWC measurements for 12 periods ranging from 17.4 minutes (73 km) to 45.3 minutes (190 km) in duration. Graphically some flight segments showed evidence of consistency with regard to clustering patterns. Cluster intensity varied from 0.06, indicating little clustering, to a high of 2.42. Cluster lengths ranged from 0.1 minutes (0.6 km) to 4.1 minutes (17.3 km). Additional analyses will allow us to determine if clustering climatologies can be developed to characterize cluster conditions by region, time period, or weather condition. Introduction

Ryerson, Charles C.↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
NASA NTRS1990

Studies of the evolution of the x ray emission of clusters of galaxies

The x ray luminosity function of clusters of galaxies was determined at different cosmic epoches using data from the Einstein Observatory Extended Medium Survey. The sample consisted of 67 x ray selected clusters that were grouped into three redshift shells. Evolution was detected in the x ray properties of clusters. The present volume density of high luminosity clusters was found to be greater than it was in the past. This result is the first convincing evidence for evolution in the x ray properties of clusters. Investigations into the constraints provided by these data on various Cold Dark Matter models are underway.

Henry, J. Patrick↗
DOE OSTI2012

A CHANDRA-VLA INVESTIGATION OF THE X-RAY CAVITY SYSTEM AND RADIO MINI-HALO IN THE GALAXY CLUSTER RBS 797

We present a study of the cavity system in the galaxy cluster RBS 797 based on Chandra and Very Large Array (VLA) data. RBS 797 (z = 0.35) is one of the most distant galaxy clusters in which two pronounced X-ray cavities have been discovered. The Chandra data confirm the presence of a cool core and indicate a higher metallicity along the cavity directions. This is likely due to the active galactic nucleus outburst, which lifts cool metal-rich gas from the center along the cavities, as seen in other systems. We find indications that the cavities are hotter than the surrounding gas. Moreover, the new Chandra images show bright rims contrasting with the deep, X-ray deficient cavities. The likely cause is that the expanding 1.4 GHz radio lobes have displaced the gas, compressing it into a shell that appears as bright cool arms. Finally, we show that the large-scale radio emission detected with our VLA observations may be classified as a radio mini-halo, powered by the cooling flow, as it nicely follows the trend P{sub radio} versus P{sub CF} predicted by the reacceleration model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

TIME-SERIES BVI PHOTOMETRY FOR THE GLOBULAR CLUSTER NGC 6981 {sup ,} {sup ,}

We present new BVI photometry of the globular cluster NGC 6981, based mostly on ground-based CCD archival images. We present a new color-magnitude diagram (CMD) that reaches almost four magnitudes below the turn-off level. We performed new derivations of metallicity and morphological parameters of the evolved sequences, in good agreement with the results of previous authors, and obtain a value of [Fe/H] ≅ –1.50 in the new UVES scale. We also identify the cluster's blue straggler population. Comparing the radial distribution of these stars with the red giant branch population, we find that the blue stragglers are more centrally concentrated, as found in previous studies of blue stragglers in globular clusters. Taking advantage of the large field of view covered by our study, we analyzed the surface density profile of the cluster, and find extratidal main sequence stars out to r ≈ 14.'1, or about twice the tidal radius. We speculate that the presence of these stars may be due to tidal disruption in the course of NGC 6981's orbit, in which case tidal tails associated with the cluster may exist. We also take a fresh look at the variable stars in the cluster, recovering all previously known variables, including three SX Phoenicis stars. We also add three previously unknown RR Lyrae (one c-type and two ab-type) to the total census. Finally, comparing our CMD with unpublished data for M3 (NGC 5272), a cluster with a similar metallicity and horizontal branch morphology, we found that both objects are essentially coeval.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

RELICS: A Very Large ( θ E ~ 40") Cluster Lens—RXC J0032.1+1808

Extensive surveys with the Hubble Space Telescope over the past decade, targeting some of the most massive clusters in the sky, have uncovered dozens of galaxy cluster strong lenses. The massive cluster strong-lens scale is typically θ E ~ 10'' to ~30''–35'', with only a handful of clusters known with Einstein radii θ E ~ 40'' or above (for z source = 2, nominally). Here we report another very large cluster lens, RXC J0032.1+1808 (z = 0.3956), the second-richest cluster in the redMapper cluster catalog and the 85th most massive cluster in the Planck Sunyaev–Zel'dovich catalog. With our light-traces-mass and fully parametric approaches, we construct strong-lensing models based on 18 multiple images of five background galaxies newly identified in the Hubble data, mainly from the Reionization Lensing Cluster Survey (RELICS), in addition to a known sextuply imaged system in this cluster. Furthermore, we compare these models to Lenstool and GLAFIC models that were produced independently as part of the RELICS program. All models reveal a large effective Einstein radius of θ E ≃ 40'' (z source = 2), owing to the obvious concentration of substructures near the cluster center. Although RXC J0032.1+1808 has a very large critical area and high lensing strength, only three magnified high-redshift candidates are found within the field targeted by RELICS. Nevertheless, we expect many more high-redshift candidates will be seen in wider and deeper observations with Hubble or the James Webb Space Telescope. Finally, the comparison between several algorithms demonstrates that the total error budget is largely dominated by systematic uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Cosmology with galaxy–galaxy lensing on non-perturbative scales: emulation method and application to BOSS LOWZ

ABSTRACT We describe our non-linear emulation (i.e. interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with N-body simulations of non-linear structure formation, designed to accurately predict the projected clustering and galaxy–galaxy lensing signals from luminous red galaxies in the redshift range 0.16 < z < 0.36 on comoving scales 0.6 < rp < 30 $h^{-1} \, \text{Mpc}$. The interpolation accuracy is ≲ 1–2 per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter S8 = (σ8/0.8228)(Ωm/0.3107)0.6 from mock measurements produced via subhalo abundance matching (SHAM)-based light-cones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over wCDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of S8 = 0.847 ± 0.037, in 3.5σ tension with the Planck cosmological results of 1.00 ± 0.02. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2022

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

60 APPLIED LIFE SCIENCES↗
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↗