32,410 research outputs found

    CLASH: Weak-Lensing Shear-and-Magnification Analysis of 20 Galaxy Clusters

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    We present a joint shear-and-magnification weak-lensing analysis of a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters at 0.19<z<0.69 selected from the Cluster Lensing And Supernova survey with Hubble (CLASH). Our analysis uses wide-field multi-color imaging, taken primarily with Suprime-Cam on the Subaru Telescope. From a stacked shear-only analysis of the X-ray-selected subsample, we detect the ensemble-averaged lensing signal with a total signal-to-noise ratio of ~25 in the radial range of 200 to 3500kpc/h. The stacked tangential-shear signal is well described by a family of standard density profiles predicted for dark-matter-dominated halos in gravitational equilibrium, namely the Navarro-Frenk-White (NFW), truncated variants of NFW, and Einasto models. For the NFW model, we measure a mean concentration of c200c=4.010.32+0.35c_{200c}=4.01^{+0.35}_{-0.32} at M200c=1.340.09+0.101015MM_{200c}=1.34^{+0.10}_{-0.09} 10^{15}M_{\odot}. We show this is in excellent agreement with Lambda cold-dark-matter (LCDM) predictions when the CLASH X-ray selection function and projection effects are taken into account. The best-fit Einasto shape parameter is αE=0.1910.068+0.071\alpha_E=0.191^{+0.071}_{-0.068}, which is consistent with the NFW-equivalent Einasto parameter of 0.18\sim 0.18. We reconstruct projected mass density profiles of all CLASH clusters from a joint likelihood analysis of shear-and-magnification data, and measure cluster masses at several characteristic radii. We also derive an ensemble-averaged total projected mass profile of the X-ray-selected subsample by stacking their individual mass profiles. The stacked total mass profile, constrained by the shear+magnification data, is shown to be consistent with our shear-based halo-model predictions including the effects of surrounding large-scale structure as a two-halo term, establishing further consistency in the context of the LCDM model.Comment: Accepted by ApJ on 11 August 2014. Textual changes to improve clarity (e.g., Sec.3.2.2 "Number-count Depletion", Sec.4.3 "Shape Measurement", Sec.4.4 "Background Galaxy Selection"). Results and conclusions remain unchanged. For the public release of Subaru data, see http://archive.stsci.edu/prepds/clash

    Survey for Transiting Extrasolar Planets in Stellar Systems: III. A Limit on the Fraction of Stars with Planets in the Open Cluster NGC 1245

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    We analyze a 19-night photometric search for transiting extrasolar planets in the open cluster NGC 1245. An automated transit search algorithm with quantitative selection criteria finds six transit candidates; none are bona fide planetary transits. We characterize the survey detection probability via Monte Carlo injection and recovery of realistic limb-darkened transits. We use this to derive upper limits on the fraction of cluster members with close-in Jupiter-radii, RJ, companions. We carefully analyze the random and systematic errors of the calculation. For similar photometric noise and weather properties as this survey, observing NGC 1245 twice as long results in a tighter constraint on "Hot Jupiter", HJ, companions than observing an additional cluster of similar richness as NGC 1245 for the same length of time as this survey. This survey observed ~870 cluster members. If 1% of stars have 1.5 RJ HJ companions, we expect to detect one planet for every 5000 dwarf stars observed for a month. To reach a ~2% upper limit on the fraction of stars with 1.5 RJ HJ companions, we conclude a total sample size of ~7400 dwarf stars observed for at least a month will be needed. Results for 1.0 RJ companions, without substantial improvement in the photometric precision, will require a small factor larger sample size.Comment: 24 pages, 15 figures, submitted A

    Detection of Enhancement in Number Densities of Background Galaxies due to Magnification by Massive Galaxy Clusters

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    We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE) inferred masses in a sample of 19 galaxy clusters with median redshift z0.42z\simeq0.42 selected from the South Pole Telescope SPT-SZ survey. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts zmedian0.9{z}_{\mathrm{median}}\simeq0.9 (low-zz background) and zmedian1.8{z}_{\mathrm{median}}\simeq1.8 (high-zz background). Stacking these populations, we detect the magnification bias effect at 3.3σ3.3\sigma and 1.3σ1.3\sigma for the low- and high-zz backgrounds, respectively. We fit NFW models simultaneously to all observed magnification bias profiles to estimate the multiplicative factor η\eta that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in η\eta resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting η\eta for the combined background populations with 1σ1\sigma uncertainties is 0.83±0.24(stat)±0.074(sys)0.83\pm0.24\mathrm{(stat)}\pm0.074\mathrm{(sys)}, indicating good consistency between the lensing and the SZE-inferred masses. We use our best-fit η\eta to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. This work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.Comment: 16 pages, 10 figures, accepted for publication in MNRA

    A Comparison and Joint Analysis of Sunyaev-Zel'dovich Effect Measurements from Planck and Bolocam for a set of 47 Massive Galaxy Clusters

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    We measure the SZ signal toward a set of 47 clusters with a median mass of 9.5×10149.5 \times 10^{14} M_{\odot} and a median redshift of 0.40 using data from Planck and the ground-based Bolocam receiver. When Planck XMM-like masses are used to set the scale radius θs\theta_{\textrm{s}}, we find consistency between the integrated SZ signal, Y5R500Y_{\textrm{5R500}}, derived from Bolocam and Planck based on gNFW model fits using A10 shape parameters, with an average ratio of 1.069±0.0301.069 \pm 0.030 (allowing for the 5\simeq 5% Bolocam flux calibration uncertainty). We also perform a joint fit to the Bolocam and Planck data using a modified A10 model with the outer logarithmic slope β\beta allowed to vary, finding β=6.13±0.16±0.76\beta = 6.13 \pm 0.16 \pm 0.76 (measurement error followed by intrinsic scatter). In addition, we find that the value of β\beta scales with mass and redshift according to βM0.077±0.026×(1+z)0.06±0.09\beta \propto M^{0.077 \pm 0.026} \times (1+z)^{-0.06 \pm 0.09}. This mass scaling is in good agreement with recent simulations. We do not observe the strong trend of β\beta with redshift seen in simulations, though we conclude that this is most likely due to our sample selection. Finally, we use Bolocam measurements of Y500Y_{500} to test the accuracy of the Planck completeness estimate. We find consistency, with the actual number of Planck detections falling approximately 1σ1 \sigma below the expectation from Bolocam. We translate this small difference into a constraint on the the effective mass bias for the Planck cluster cosmology results, with (1b)=0.93±0.06(1-b) = 0.93 \pm 0.06.Comment: Updated to include one additional co-author. Also some minor changes to the text based on initial feedbac

    Nonparametric Feature Extraction from Dendrograms

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    We propose feature extraction from dendrograms in a nonparametric way. The Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the sequential combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies
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