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This report examines the spatial and spectral clustering technique for the unsupervised automatic classification and mapping of earth resources satellite data, and makes theoretical analysis of the decision rules and tests in order to suggest how the method might best be applied to other flight data such as Skylab and Spacelab.
The Hubble diagram for the 561 Abell clusters with measured redshifts has been examined using Abell's (1958) corrected photo-red magnitudes for the tenth-ranked cluster member (m10). After correction for the Scott effect and K dimming, the data are in good agreement with a linear magnitude-redshift relation with a slope of 0.2 out to z = 0.1. New redshift data are also presented for 20 Abell clusters. Abell's m10 is suitable for redshift estimation for clusters with m10 of no more than 16.5. At fainter m10, the number of foreground galaxies expected within an Abell radius is large enough to make identification of the tenth-ranked galaxy difficult. Interlopers bias the estimated redshift toward low values at high redshift. Leir and van den Bergh's (1977) redshift estimates suffer from this same bias but to a smaller degree because of the use of multiple cluster parameters. Constraints on deviations of cluster velocities from the mean cosmological flow require greater photometric accuracy than is provided by Abell's m10 magnitudes.
The effects of cluster correlations have been studied in the 12 C + 12 C reaction at 50 MeV/nucleon, using three AMD models, the AMD (AMD/D) without any additional cluster correlations, AMD/D-COALS with nucleon correlations based on a coalescence prescription for light cluster formations with A ≤ 4 and AMD-Cluster with an extended cluster correlation in two-nucleon collision processes and a special treatment for intermediate fragment formation with A ≤ 9. The angular distributions and energy spectra of fragments have been simulated and compared with the available experimental data. It is found that the cluster correlations take a crucial role to describe the productions of light charged particles (LCPs) and intermediate mass fragments (IMFs), and the AMD-cluster studied here provides a consistent overall reproduction of the experimental data. It is also shown that the significant effects of the secondary decay processes are involved for the fragment production besides the dynamical productions in the AMD stage. Detailed LCP and IMF production mechanisms involved in the intermediate energy heavy ion collisions are discussed.
Here we present the analysis of multifrequency data gathered for the Fanaroff-Riley type-II (FR II) radio galaxy PKS B1358-113, hosted in the brightest cluster galaxy in the center of A1836. The galaxy harbors one of the most massive black holes known to date, and our analysis of the acquired optical data reveals that this black hole is only weakly active, with a mass accretion rate M-dot {sub acc}∼2×10{sup −4} M-dot {sub Edd}∼0.02 M{sub ⊙} yr{sup –1}. Based on analysis of new Chandra and XMM-Newton X-ray observations and archival radio data, and assuming the well-established model for the evolution of FR II radio galaxies, we derive the preferred range for the jet kinetic luminosity L {sub j} ∼ (1-6) × 10{sup –3} L {sub Edd} ∼ (0.5-3) × 10{sup 45} erg s{sup –1}. This is above the values implied by various scaling relations proposed for radio sources in galaxy clusters, being instead very close to the maximum jet power allowed for the given accretion rate. We also constrain the radio source lifetime as τ{sub j} ∼ 40-70 Myr, meaning the total amount of deposited jet energy E {sub tot} ∼ (2-8) × 10{sup 60} erg. We argue that approximately half of this energy goes into shock heating of the surrounding thermal gas, and the remaining 50% is deposited into the internal energy of the jet cavity. The detailed analysis of the X-ray data provides indication for the presence of a bow shock driven by the expanding radio lobes into the A1836 cluster environment. We derive the corresponding shock Mach number in the range M{sub sh}∼2--4, which is one of the highest claimed for clusters or groups of galaxies. This, together with the recently growing evidence that powerful FR II radio galaxies may not be uncommon in the centers of clusters at higher redshifts, supports the idea that jet-induced shock heating may indeed play an important role in shaping the properties of clusters, galaxy groups, and galaxies in formation. In this context, we speculate on a possible bias against detecting stronger jet-driven shocks in poorer environments, resulting from inefficient electron heating at the shock front, combined with a relatively long electron-ion temperature equilibration timescale.
Abstract In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack , the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.
Two test areas, representing a variety of avalanche hazards, were selected in the San Juan Mountains of Colorado. Midwinter Landsat digital data were analyzed using a clustering technique, and the results compared to 1:24,000 scale maps of avalanche hazards derived from air photo interpretation and field surveys. Confined avalanches were readily identified because of the high contrast between the snow covered avalanche track and the adjacent forested slopes. Unconfined avalanches could not be identified without supplementary topographic data. Spatial characteristics were of primary importance in delineating avalanche tracks. Spatial resolution was the limiting factor in avalanche detection. Landsat data should prove useful for rapid reconnaissance mapping of avalanche hazards, particularly in the absence of other data sources.
This paper is the first in a series, presenting a new galaxy cluster finder based on a three-dimensional Voronoi Tesselation plus a maximum likelihood estimator, followed by gapping-filtering in radial velocity(VoML+G). The scientific aim of the series is a reassessment of the diversity of optical clusters in the local universe. A mock galaxy database mimicking the southern strip of the magnitude(blue)-limited 2dF Galaxy Redshift Survey (2dFGRS), for the redshift range 0.009 < z < 0.22, is built on the basis of the Millennium Simulation of the LCDM cosmology and a reference catalog of “Millennium clusters,” spannning across the 1.0 × 10{sup 12}–1.0 × 10{sup 15} M {sub ⊙} h {sup −1} dark matter (DM) halo mass range, is recorded. The validation of VoML+G is performed through its application to the mock data and the ensuing determination of the completeness and purity of the cluster detections by comparison with the reference catalog. The execution of VoML+G over the 2dFGRS mock data identified 1614 clusters, 22% with N {sub g} ≥ 10, 64 percent with 10 > N {sub g} ≥ 5, and 14% with N {sub g} < 5. The ensemble of VoML+G clusters has a ∼59% completeness and a ∼66% purity, whereas the subsample with N {sub g} ≥ 10, to z ∼ 0.14, has greatly improved mean rates of ∼75% and ∼90%, respectively. The VoML+G cluster velocity dispersions are found to be compatible with those corresponding to “Millennium clusters” over the 300–1000 km s{sup −1} interval, i.e., for cluster halo masses in excess of ∼3.0 × 10{sup 13} M {sub ⊙} h {sup −1}.
Abstract Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO 2 (111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.
In current and future large redshift surveys, as the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (SDSS-IV/eBOSS) or the Dark Energy Spectroscopic Instrument (DESI), we will use emission-line galaxies (ELGs) to probe cosmological models by mapping the large-scale structure of the Universe in the redshift range 0.6 < z < 1.7. We explore the halo-galaxy connection, with current data and by measuring three clustering properties of g-selected ELGs as matter tracers in the redshift range 0.6 < z < 1: (i) the redshift-space two-point correlation function using spectroscopic redshifts from the BOSS ELG sample and VIPERS; (ii) the angular two-point correlation function on the footprint of the CFHT-LS; (iii) the galaxy-galaxy lensing signal around the ELGs using the CFHTLenS. Furthermore, we interpret these observations by mapping them on to the latest high-resolution MultiDark Planck N-body simulation, using a novel (Sub)Halo-Abundance Matching technique that accounts for the ELG incompleteness. ELGs at z ~ 0.8 live in haloes of (1 ± 0.5) × 10 12 h -1 M⊙ and 22.5 ± 2.5 per cent of them are satellites belonging to a larger halo. The halo occupation distribution of ELGs indicates that we are sampling the galaxies in which stars form in the most efficient way, according to their stellar-to-halo mass ratio.
Using Hubble Space Telescope/Wide Field Planetary Camera 2 data, we have performed a comparative study of the Blue Straggler Star (BSS) populations in the central regions of the globular clusters (GCs) AM 1, Eridanus, Palomar 3, and Palomar 4. Located at distances R{sub GC} > 50 kpc from the Galactic center, these are (together with Palomar 14 and NGC 2419) the most distant clusters in the halo. We determine their color-magnitude diagrams and centers of gravity. The four clusters turn out to have similar ages (10.5-11 Gyr), significantly smaller than those of the inner-halo globulars, and similar metallicities. By exploiting wide-field ground-based data, we build the most extended radial density profiles from resolved star counts ever published for these systems. These are well reproduced by isotropic King models of relatively low concentration. BSSs appear to be significantly more centrally segregated than red giants in all GCs, in agreement with the estimated core and half-mass relaxation times which are smaller than the cluster ages. Assuming that this is a signature of mass segregation, we conclude that AM 1 and Eridanus are slightly dynamically more evolved than Pal 3 and Pal 4.
To probe star formation (SF) processes, we present results of an analysis of the molecular cloud G35.20−0.74 (hereafter MCG35.2) using multi-frequency observations. The MCG35.2 is depicted in a velocity range of 30–40 km s{sup −1}. An almost horseshoe-like structure embedded within the MCG35.2 is evident in the infrared and millimeter images and harbors the previously known sites, ultra-compact/hyper-compact G35.20−0.74N H ii region, Ap2-1, and Mercer 14 at its base. The site, Ap2-1, is found to be excited by a radio spectral type of B0.5V star where the distribution of 20 cm and Hα emission is surrounded by the extended molecular hydrogen emission. Using the Herschel 160–500 μm and photometric 1–24 μm data analysis, several embedded clumps and clusters of young stellar objects (YSOs) are investigated within the MCG35.2, revealing the SF activities. A majority of the YSOs clusters and massive clumps (500–4250 M{sub ⊙}) are seen toward the horseshoe-like structure. The position–velocity analysis of {sup 13}CO emission shows a blueshifted peak (at 33 km s{sup −1}) and a redshifted peak (at 37 km s{sup −1}) interconnected by lower intensity intermediate velocity emission, tracing a broad bridge feature. The presence of such a broad bridge feature suggests the onset of a collision between molecular components in the MCG35.2. A noticeable change in the H-band starlight mean polarization angles has also been observed in the MCG35.2, probably tracing the interaction between molecular components. Taken together, it seems that the cloud–cloud collision process has influenced the birth of massive stars and YSOs clusters in the MCG35.2.
Wide-field and deep DECam multiband photometry, combined with HST data for the core of the Galactic globular cluster NGC 2808, allowed us to study the distribution of various stellar subpopulations and stars in different evolutionary phases out to the cluster tidal radius. We used the C ugi = (u - g) - (g - i) index to identify three chemically distinct subpopulations along the red giant branch and compared their spatial distributions. The most light-element-enriched subpopulation (P3) is more centrally concentrated; however, it shows a more extended distribution in the external regions of the cluster compared to the primordial (P1) and intermediate (P2) composition populations. Furthermore, the P3 subpopulation centroid is off-center relative to those of the P1 and P2 groups. We also analyzed the spatial distribution of horizontal branch stars and found that the relative fraction of red horizontal branch stars increases for radial distances larger than ≈1$^{'}_{.}$5, while that of the blue and hotter stars decreases. These new observations, combined with literature spectroscopic measurements, suggest that the red horizontal branch stars are the progeny of all the stellar subpopulations in NGC 2808, i.e., primordial and light-element enhanced, while the blue stars are possibly the result of a combination of the "hot-flasher" and the "helium-enhanced" scenarios. A similar distribution of different red giant branch subpopulations and horizontal branch stars was also found for the most massive Galactic globular cluster, ω Cen, based on combined DECam and HST data, which suggests that the two may share a similar origin.
We present a study of the weak lensing inferred matter profiles ΔΣ(R) of 698 South Pole Telescope (SPT) thermal Sunyaev-Zel’dovich effect (tSZE) selected and MCMF optically confirmed galaxy clusters in the redshift range 0.25 < z < 0.94 that have associated weak gravitational lensing shear profiles from the Dark Energy Survey (DES). Rescaling these profiles to account for the mass dependent size and the redshift dependent density produces average rescaled matter profiles ΔΣ(R/R200c)/(ρcritR200c) with a lower dispersion than the unscaled ΔΣ(R) versions, indicating a significant degree of self-similarity. Galaxy clusters from hydrodynamical simulations also exhibit matter profiles that suggest a high degree of self-similarity, with RMS variation among the average rescaled matter profiles with redshift and mass falling by a factor of approximately six and 23, respectively, compared to the unscaled average matter profiles. We employed this regularity in a new Bayesian method for weak lensing mass calibration that employs the so-called cluster mass posterior P(M200|ζ̂, λ̂, z), which describes the individual cluster masses given their tSZE (ζ̂) and optical (λ̂, z) observables. This method enables simultaneous constraints on richness λ-mass and tSZE detection significance ζ-mass relations using average rescaled cluster matter profiles. We validated the method using realistic mock datasets and present observable-mass relation constraints for the SPT×DES sample, where we constrained the amplitude, mass trend, redshift trend, and intrinsic scatter. Our observable-mass relation results are in agreement with the mass calibration derived from the recent cosmological analysis of the SPT×DES data based on a cluster-by-cluster lensing calibration. Our new mass calibration technique offers a higher efficiency when compared to the single cluster calibration technique. We present new validation tests of the observable-mass relation that indicate the underlying power-law form and scatter are adequate to describe the real cluster sample but that also suggest a redshift variation in the intrinsic scatter of the λ-mass relation may offer a better description. In addition, the average rescaled matter profiles offer high signal-to-noise ratio (S/N) constraints on the shape of real cluster matter profiles, which are in good agreement with available hydrodynamical ΛCDM simulations. This high S/N profile contains information about baryon feedback, the collisional nature of dark matter, and potential deviations from general relativity.Key words: gravitational lensing: weak / galaxies: clusters: general / large-scale structure of Universe
We present the first study of the spatial distribution of star formation in z ∼ 0.5 cluster galaxies. The analysis is based on data taken with the Wide Field Camera 3 as part of the Grism Lens-Amplified Survey from Space (GLASS). We illustrate the methodology by focusing on two clusters (MACS 0717.5+3745 and MACS 1423.8+2404) with different morphologies (one relaxed and one merging) and use foreground and background galaxies as a field control sample. The cluster+field sample consists of 42 galaxies with stellar masses in the range 10{sup 8}–10{sup 11} M{sub ⊙} and star formation rates in the range 1–20 M{sub ⊙} yr{sup −1}. Both in clusters and in the field, Hα is more extended than the rest-frame UV continuum in 60% of the cases, consistent with diffuse star formation and inside-out growth. In ∼20% of the cases, the Hα emission appears more extended in cluster galaxies than in the field, pointing perhaps to ionized gas being stripped and/or star formation being enhanced at large radii. The peak of the Hα emission and that of the continuum are offset by less than 1 kpc. We investigate trends with the hot gas density as traced by the X-ray emission, and with the surface mass density as inferred from gravitational lens models, and find no conclusive results. The diversity of morphologies and sizes observed in Hα illustrates the complexity of the environmental processes that regulate star formation. Upcoming analysis of the full GLASS data set will increase our sample size by almost an order of magnitude, verifying and strengthening the inference from this initial data set.
Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.
This paper introduces a new parallel clustering algorithm, named Feel-the-Way clustering algorithm, that provides better or equivalent convergence rate than the traditional clustering methods by optimizing the synchronization and communication costs. Our algorithm design centers on how to optimize three factors simultaneously: reduced synchronizations, improved convergence rate, and retained same or comparable optimization cost. To compare the optimization cost, we use the Sum of Square Error (SSE) cost as the metric, which is the sum of the square distance between each data point and its assigned clusters. Compared with the traditional MPI k-means algorithm, the new Feel-the-Way algorithm requires less communications among participating processes. As for the convergence rate, the new algorithm requires fewer number of iterations to converge. As for the optimization cost, it obtains the SSE costs that are close to the k-means algorithm. In the paper, we first design the full-step Feel-the-Way k-means clustering algorithm that can significantly reduce the number of iterations that are required by the original k-means clustering method. Next, we improve the performance of the full-step algorithm by adopting an optimized sampling-based approach, named reassignment-history-aware sampling. Our experimental results show that the optimized sampling-based Feel-the-Way method is significantly faster than the widely used k-means clustering method, and can provide comparable optimization costs. More extensive experiments with several synthetic datasets and real-world datasets (e.g., MNIST, CIFAR-10, ENRON, and PLACES-2) show that the new parallel algorithm can outperform the open source MPI k-means library by up to 110% on a high-performance computing system using 4,096 CPU cores. In addition, the new algorithm can take up to 51% fewer iterations to converge than the k-means clustering algorithm.
Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.
Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.