Results for “data clustering” — Page 46

NASA NTRS1993

Deep, wide-field, multi-band imaging of z approximately equal to 0.4 clusters and their environs

The existence of an excess population of blue galaxies in the cores of distant, rich clusters of galaxies, commonly referred to as the 'Butcher-Oemler' effect is now well established. Spectroscopy of clusters at z = 0.2-0.4 has confirmed that the luminous blue populations comprise as much as 20 percent of these clusters. This fraction is much higher that the 2 percent blue fraction found for nearby rich clusters, such as Coma, indicating that rapid galaxy evolution has occurred on a relatively short time scale. Spectroscopy has also shown that the 'blue' galaxies can basically be divided into three classes: 'starburst' galaxies with large (O II) equivalent widths, 'post-starburst' E+A galaxies (i.e. galaxies with strong Balmer lines shortward of 4000A but elliptical-like colors, and normal spiral/irregulars. Unfortunately, it is difficult to obtain enough spectra of individual galaxies in these intermediate redshift clusters to say anything statistically meaningful. Thus, limited information is available about the relative numbers of these three classes of 'blue' galaxies and the associated E/SO population in these intermediate redshift clusters. More statistically meaningful results can be derived from deep imaging of these clusters. However, the best published data to date (e.g. MacLaren et al. 1988; Dressler & Gunn 1992) are limited to the cluster cores and do not sample the galaxy luminosity functions very deeply at the bluest wavelengths. Furthermore, only limited spectro-energy distribution data is available below 4000A in the observed cluster rest frame providing limited sensitivity to 'recent' star formation activity. To improve this situation, we are currently obtaining deep, wide-field UBRI images of all known rich clusters at z approx. equals 0.4. Our main objective is to obtain the necessary color information to distinguish between the E+SO, 'E+A', and spiral/irregular galaxy populations throughout the cluster/supercluster complex. At this redshift, UBRI correspond to rest-frame 2500A/UVR bandpasses. The rest-frame UVR system provides a powerful 'blue' galaxy discriminate given the expected color distribution. Moreover, since 'hot' stars peak near 2500A, that bandpass is a powerful probe of recent star formation activity in all classes of galaxies. In particular, it is sensitive to ellipticals with 'UV excess' populations (MacLaren et al. 1988).

Silva, David R.↗

DOE OSTI2020

Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning

The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

The Open Cluster Chemical Abundances and Mapping Survey. VII. APOGEE DR17 [C/N]–Age Calibration

Large-scale surveys open the possibility to investigate Galactic evolution both chemically and kinematically; however, reliable stellar ages remain a major challenge. Detailed chemical information provided by high-resolution spectroscopic surveys of the stars in clusters can be used as a means to calibrate recently developed chemical tools for age-dating field stars. Using data from the Open Cluster Abundances and Mapping survey, based on the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment 2 survey, we derive a new empirical relationship between open cluster stellar ages and the carbon-to-nitrogen ([C/N]) abundance ratios for evolved stars, primarily those on the red giant branch. With this calibration, [C/N] can be used as a chemical clock for evolved field stars to investigate the formation and evolution of different parts of our Galaxy. We explore how mixing effects at different stellar evolutionary phases, like the red clump, affect the derived calibration. We have established the [C/N]–age calibration for APOGEE Data Release 17 (DR17) giant star abundances to be $\mathrm{log}{[\mathrm{Age}(\mathrm{yr})]}_{\mathrm{DR}17}=10.14\,(\pm 0.08)+2.23(\pm 0.19)\,[{\rm{C}}/{\rm{N}}]$, usable for $8.62\leqslant \mathrm{log}(\mathrm{Age}[\mathrm{yr}])\leqslant 9.82$, derived from a uniform sample of 49 clusters observed as part of APOGEE DR17 applicable primarily to metal-rich, thin- and thick-disk giant stars. This measured [C/N]–age APOGEE DR17 calibration is also shown to be consistent with asteroseismic ages derived from Kepler photometry.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1989

Abundances of the elements in the intergalactic medium

A summary of the X-ray and optical spectroscopic data on emission lines from elements in clusters of galaxies is presented. The best data exists for Fe. For the about 30 well-observed clusters, the mean Fe abundance is about 0.4 solar, but there appears to be a real variation of at least a factor of 2. X-ray emission lines from O, Mg, Si and S have been detected, and their abundances are within a factor of a few of solar. Analysis of optical emission lines from cooling gas due to O, N, and S also indicates abundances not too far from solar values. The implications of the presence of heavy elements in the intergalactic medium of clusters for the evolution of the chemical abundances of galaxies are discussed.

Mushotzky, Richard↗

DOE OSTI2014

Diverse Power Iteration Embeddings and Its Applications

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detection and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Characterization of ferredoxins involved in electron transfer pathways for nitrogen fixation implicates differences in electronic structure in tuning 2[4Fe 4S] Fd activity

Ferredoxins (Fds) are small proteins which shuttle electrons to pathways like biological nitrogen fixation. Physical properties tune the reactivity of Fds with different pathways, but knowledge on how these properties can be manipulated to engineer new electron transfer pathways is lacking. Recently, we showed that an evolved strain of Rhodopseudomonas palustris uses a new electron transfer pathway for nitrogen fixation. This pathway involves a variant of the primary Fd of nitrogen fixation in R. palustris, Fer1, in which threonine at position 11 is substituted for isoleucine (Fer1 T11I ). To understand why this substitution in Fer1 enables more efficient electron transfer, we used in vivo and in vitro methods to characterize Fer1 and Fer1 T11I . Electrochemical characterization revealed both Fer1 and Fer1 T11I have similar redox transitions (–480 mV and – 550 mV), indicating the reduction potential was unaffected despite the proximity of T11 to an iron-sulfur (Fe—S) cluster of Fer1. Additionally, disruption of hydrogen bonding around an Fe—S cluster in Fer1 by substituting threonine with alanine (T11A) or valine (T11V) did not increase nitrogenase activity, indicating that disruption of hydrogen bonding does not explain the difference in activity observed for Fer1 T11I . Electron paramagnetic resonance spectroscopy studies revealed key differences in the electronic structure of Fer1 and Fer1 T11I , which indicate changes to the high spin states and/or spin-spin coupling between the Fe—S clusters of Fer1. Finally, our data implicates these electronic structure differences in facilitating electron flow and sets a foundation for further investigations to understand the connection between these properties and intermolecular electron transfer.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1978

An energy-dependent map of the X-ray emission of the Perseus cluster

Results are discussed for X-ray scans of the Perseus cluster with the SAS 3 slat detectors. Counting rates in two broadband channels (1.5 to 5.5 keV and 5.5 to 15.5 keV) are compared as a function of the instrument scan angle measured from the center of emission, and it is found that the emission in the higher energy range is broader in extent. Tests for a systematic energy-dependent error in the background demonstrate that the discovered energy dependence of the Perseus cluster emission is valid. Iron line emission is determined by two methods, various models are fitted to the data, and the total cluster emission in the two energy ranges is computed. The iron line emission is determined to be 0.003 photon/sq cm per sec. It is concluded that the diffuse emission increases in the range from 1.5 to 15.5 keV within the bounds of a model involving a point source with a radius not exceeding about 3 arcmin plus a diffuse source.

Ulmer, M. P.↗

DOE OSTI2022

Tracer-field cross-correlations with k -nearest neighbour distributions

ABSTRACT In astronomy and cosmology significant effort is devoted to characterizing and understanding spatial cross-correlations between points – e.g galaxy positions, high energy neutrino arrival directions, X-ray and AGN sources, and continuous fields – e.g. weak lensing meiand Cosmic Microwave Background maps. Recently, we introduced the k-nearest neighbour (kNN) formalism to better characterize the clustering of discrete (point) data sets. Here, we extend it to the point – field cross-correlations analysis. It combines kNN measurements of the point data set with measurements of the field smoothed at many scales. The resulting statistics are sensitive to all orders in the joint clustering of the points and the field. We demonstrate that this approach, unlike the 2-pt cross-correlation, can measure the statistical dependence of two data sets even when there are no linear (Gaussian) correlations between them. We further demonstrate that this framework is far more effective than the two point function in detecting cross-correlations when the continuous field is contaminated by high levels of noise. For a particularly high level of noise, the cross-correlation between haloes and the underlying matter field in a cosmological simulation, between 10 h−1 Mpc and 30 h−1 Mpc, is detected at >5σ significance using the technique presented here, when the two-point cross-correlation significance is ∼1σ. Finally, we show that kNN cross-correlations of haloes and the matter field can be well modelled on quasi-linear scales using the Hybrid Effective Field Theory (HEFT) framework, with the same set of bias parameters as are used for 2-pt cross-correlations. The substantial improvement in the statistical power of detecting cross-correlations using this method makes it a promising tool for various cosmological applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

PHAT stellar cluster survey. II. Andromeda Project cluster catalog

We construct a stellar cluster catalog for the Panchromatic Hubble Andromeda Treasury (PHAT) survey using image classifications collected from the Andromeda Project citizen science website. We identify 2753 clusters and 2270 background galaxies within ∼0.5 deg{sup 2} of PHAT imaging searched, or ∼400 kpc{sup 2} in deprojected area at the distance of the Andromeda Galaxy (M31). These identifications result from 1.82 million classifications of ∼20,000 individual images (totaling ∼7 gigapixels) by tens of thousands of volunteers. We show that our crowd-sourced approach, which collects >80 classifications per image, provides a robust, repeatable method of cluster identification. The high spatial resolution Hubble Space Telescope images resolve individual stars in each cluster and are instrumental in the factor of ∼6 increase in the number of clusters known within the survey footprint. We measure integrated photometry in six filter passbands, ranging from the near-UV to the near-IR. PHAT clusters span a range of ∼8 magnitudes in F475W (g-band) luminosity, equivalent to ∼4 decades in cluster mass. We perform catalog completeness analysis using >3000 synthetic cluster simulations to determine robust detection limits and demonstrate that the catalog is 50% complete down to ∼500 M{sub ⊙} for ages <100 Myr. We include catalogs of clusters, background galaxies, remaining unselected candidates, and synthetic cluster simulations, making all information publicly available to the community. The catalog published here serves as the definitive base data product for PHAT cluster science, providing a census of star clusters in an L{sup ⋆} spiral galaxy with unmatched sensitivity and quality.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2001

Stellar Evolution in Starburst Galaxies

The main thrust of the program was to obtain UV spectroscopy of a number of massive and hot luminous (OB type) stars in the nearby galaxy called the Small Magellanic Cloud (SMC). The objective was to analyze their atmospheres and winds so as to determine the effect of the lower abundance of the SIVIC on these parameters. Furthermore, the differences in evolution could be investigated. Additionally, the UV spectra themselves would be suitably weighted and systematically combined to provide a template for comparison to very distant galaxies formed in the early history of the Universe which also have a low abundance of elements. The spectra have been obtained and the analysis is proceeding, primarily by the groups in Munich and at STScl who are the leads for this project. Given the important role of the nearby SMC galaxy as a template of low metal abundance, I have begun to investigate the YOUNGEST phases of massive star birth, before the most massive and hottest stars become optically visible. Typically these stars form in clusters, in some cases having tens to hundreds of OB type stars. In this phase, each star is still buried in its natal cloud and visible only in the infrared (IR) from its self-heated dust and/or from radio free-free emission of the surrounding hydrogen (HII) region. Efforts to find and identify these buried clusters were conducted using a large radio telescope. A number of these were found and further analysis of the data is underway. These clusters are not visible optically, but ought to be seen in the IR, and are a likely topic for HST photometry on NICMOS. A proposal to do this will be made next semester. These objects are the precursors of the optically visible clusters that contain massive and hot luminous stars.

Conti, Peter↗

DOE OSTI2019

Trajectory analysis with geometric features

Described herein are various technologies pertaining to extracting one or more features from trajectory data recorded during motion of a body, and further, generating a n-dimensional feature vector based upon the one or more extracted features. The n-dimensional feature vector enables expedited analysis of the trajectory data from which the feature vector was generated. For example, rather than having to analyze a trajectory curve comprising a large number of time-position data points, the n-dimensional feature vector can be compared with one or more search parameters to facilitate clustering of the trajectory data associated with the n-dimensional feature vector with other trajectory data which also satisfies the search request. The trajectory data can be plotted on a screen in combination with the n-dimensional feature vector, and other pertinent information. The trajectory data, etc., can be displayed using heat maps or other graphical representation.

Wilson, Andrew T.↗

DOE OSTI2020

Trajectory prediction via a feature vector approach

Described herein are various technologies pertaining to extracting one or more features from trajectory data recorded during motion of a body, and further, generating a n-dimensional feature vector based upon the one or more extracted features. The n-dimensional feature vector enables expedited analysis of the trajectory data from which the feature vector was generated. For example, rather than having to analyze a trajectory curve comprising a large number of time-position data points, the n-dimensional feature vector can be compared with one or more search parameters to facilitate clustering of the trajectory data associated with the n-dimensional feature vector with other trajectory data which also satisfies the search request. The trajectory data can be plotted on a screen in combination with the n-dimensional feature vector, and other pertinent information. The trajectory data, etc., can be displayed using heat maps or other graphical representation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2013

ULTRAVIOLET OBSERVATIONS OF THE GLOBULAR CLUSTER M10 FROM HST AND GALEX: THE BSS POPULATION

We present a combination of high-resolution Hubble Space Telescope and wide-field ground-based and Galaxy Evolution Explorer data of the Galactic globular cluster M10 (NGC 6254). By using this large data set, we determined the center of gravity of the cluster and we built its density profile from star counts over its entire radial extension. We find that the density profile is well reproduced by a single-mass King model with structural parameters c = 1.41 and r{sub c} = 41''. We also studied the blue straggler star (BSS) population and its radial distribution. We count a total number of 120 BSS within the tidal radius. Their radial distribution is bimodal: highly peaked in the cluster center, decreasing at intermediate distances, and rising again outward. We discuss these results in the context of the dynamical clock scheme presented by Ferraro et al. and of recent results about the radial distribution of binary systems in this cluster.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

Small-scale fluctuations and angular correlations of the X-ray background in the HEAO 1 A-2 energy band - Constraints on clustering of X-ray sources

HEAO 1 A-2 all-sky survey data have been used to determine the amplitude of intensity fluctuations of the extragalactic 2-10 keV X-ray background (XRB) over an effective solid angle of 1.84 sq deg and their angular correlation function on angular scales of less than 3 deg. A good empirical fit to the data is obtained assuming that the integral counts in the A-2 band have a slope of 1.65 + 0.06 or - 0.07. Alternatively, the data may imply a significant clustering of extragalactic X-ray sources.

Martin-Mirones, J. M.↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗

NASA NTRS1989

Cluster analysis of the hot subdwarfs in the PG survey

Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

Thejll, Peter↗

DOE OSTI2016

THE HAWAII SCUBA-2 LENSING CLUSTER SURVEY: NUMBER COUNTS AND SUBMILLIMETER FLUX RATIOS

We present deep number counts at 450 and 850 μ m using the SCUBA-2 camera on the James Clerk Maxwell Telescope. We combine data for six lensing cluster fields and three blank fields to measure the counts over a wide flux range at each wavelength. Thanks to the lensing magnification, our measurements extend to fluxes fainter than 1 mJy and 0.2 mJy at 450 μ m and 850 μ m, respectively. Our combined data highly constrain the faint end of the number counts. Integrating our counts shows that the majority of the extragalactic background light (EBL) at each wavelength is contributed by faint sources with L {sub IR} < 10{sup 12} L {sub ⊙}, corresponding to luminous infrared galaxies (LIRGs) or normal galaxies. By comparing our result with the 500 μ m stacking of K -selected sources from the literature, we conclude that the K -selected LIRGs and normal galaxies still cannot fully account for the EBL that originates from sources with L {sub IR} < 10{sup 12} L {sub ⊙}. This suggests that many faint submillimeter galaxies may not be included in the UV star formation history. We also explore the submillimeter flux ratio between the two bands for our 450 μ m and 850 μ m selected sources. At 850 μ m, we find a clear relation between the flux ratio and the observed flux. This relation can be explained by a redshift evolution, where galaxies at higher redshifts have higher luminosities and star formation rates. In contrast, at 450 μ m, we do not see a clear relation between the flux ratio and the observed flux.

79 ASTRONOMY AND ASTROPHYSICS↗