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Register a new userPrecision Measurements of the Cluster Red Sequence using an Error Corrected Gaussian Mixture Model
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The red sequence is an important feature of galaxy clusters and plays a crucial role in optical cluster detection. Measurement of the slope and scatter of the red sequence are affected both by selection of red sequence galaxies and measurement errors. In this paper, we describe a new error corrected Gaussian Mixture Model for red sequence galaxy identification. Using this technique, we can remove the effects of measurement error and extract unbiased information about the intrinsic properties of the red sequence. We use this method to select red sequence galaxies in each of the 13,823 clusters in the maxBCG catalog, and measure the red sequence ridgeline location and scatter of each. These measurements provide precise constraints on the variation of the average red galaxy populations in the observed frame with redshift. We find that the scatter of the red sequence ridgeline increases mildly with redshift, and that the slope decreases with redshift. We also observe that the slope does not strongly depend on cluster richness. Using similar methods, we show that this behavior is mirrored in a spectroscopic sample of field galaxies, further emphasizing that ridgeline properties are independent of environment.
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We present the Red-sequence Cluster Lensing Survey (RCSLenS), an application of the methods developed for the Canada France Hawaii Telescope Lensing Survey (CFHTLenS) to the ~785deg$^2$, multi-band imaging data of the Red-sequence Cluster Survey 2 (RCS2). This project represents the largest public, sub-arcsecond seeing, multi-band survey to date that is suited for weak gravitational lensing measurements. With a careful assessment of systematic errors in shape measurements and photometric redshifts we extend the use of this data set to allow cross-correlation analyses between weak lensing observables and other data sets. We describe the imaging data, the data reduction, masking, multi-colour photometry, photometric redshifts, shape measurements, tests for systematic errors, and a blinding scheme to allow for more objective measurements. In total we analyse 761 pointings with r-band coverage, which constitutes our lensing sample. Residual large-scale B-mode systematics prevent the use of this shear catalogue for cosmic shear science. The effective number density of lensing sources over an unmasked area of 571.7deg$^2$ and down to a magnitude limit of r~24.5 is 8.1 galaxies per arcmin$^2$ (weighted: 5.5 arcmin$^{-2}$) distributed over 14 patches on the sky. Photometric redshifts based on 4-band griz data are available for 513 pointings covering an unmasked area of 383.5 deg$^2$ We present weak lensing mass reconstructions of some example clusters as well as the full survey representing the largest areas that have been mapped in this way. All our data products are publicly available through CADC at http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/community/rcslens/query.html in a format very similar to the CFHTLenS data release.
Using samples drawn from the Sloan Digital Sky Survey, we study the relationship between local galaxy density and the properties of galaxies on the red sequence. After removing the mean dependence of average overdensity (or environment) on color and luminosity, we find that there remains a strong residual trend between luminosity-weighted mean stellar age and environment, such that galaxies with older stellar populations favor regions of higher overdensity relative to galaxies of like color and luminosity (and hence of like stellar mass). Even when excluding galaxies with recent star-formation activity (i.e., younger mean stellar ages) from the sample, we still find a highly significant correlation between stellar age and environment at fixed stellar mass. This residual age-density relation provides direct evidence for an assembly bias on the red sequence such that galaxies in higher-density regions formed earlier than galaxies of similar mass in lower-density environments. We discuss these results in the context of the age-metallicity degeneracy and in comparison to previous studies at low and intermediate redshift. Finally, we consider the potential role of assembly bias in explaining recent results regarding the evolution of post-starburst (or post-quenching) galaxies and the environmental dependence of the type Ia supernova rate.
We present a high-precision mass model of galaxy cluster Abell 2744, based on a strong-gravitational-lensing analysis of the emph{Hubble Space Telescope Frontier Fields} (HFF) imaging data, which now include both emph{Advanced Camera for Surveys} and emph{Wide-Field Camera 3} observations to the final depth. Taking advantage of the unprecedented depth of the visible and near-infrared data, we identify 34 new multiply imaged galaxies, bringing the total to 61, comprising 181 individual lensed images. In the process, we correct previous erroneous identifications and positions of multiple systems in the northern part of the cluster core. With the textsc{Lenstool} software and the new sets of multiple images, we model the cluster using two cluster-scale dark matter halos plus galaxy-scale halos for the cluster members. Our best-fit model predicts image positions with an emph{RMS} error of 0.69$arcsec$, which constitutes an improvement by almost a factor of two over previous parametric models of this cluster. We measure the total projected mass inside a 200~kpc aperture as ($2.162pm 0.005$)$times 10^{14}M_{odot}$, thus reaching 1% level precision for the second time, following the recent HFF measurement of MACSJ0416.1-2403. Importantly, the higher quality of the mass model translates into an overall improvement by a factor of 4 of the derived magnification factor. % for the high-redshift lensed background galaxies. Together with our previous HFF gravitational lensing analysis, this work demonstrates that the HFF data enables high-precision mass measurements for massive galaxy clusters and the derivation of robust magnification maps to probe the early Universe.
Clustering is an important tool to describe gamma-ray bursts (GRBs). We analyzed the Final BATSE Catalog using Gaussian-mixture-models-based clustering methods for six variables (durations, peak flux, total fluence and spectral hardness ratios) that contain information on clustering. Our analysis found that the five kinds of GRBs previously found by other authors are only the cut groups of the previously well-known three types (short, long and intermediate in duration). The two short and intermediate duration groups differ mostly in the peak flux. Therefore, the reanalysis of the BATSE data finds similar group structures than previously. Because the brightness distribution is asymmetric and not correlated with durations or hardnesses the Gaussian mixture model cuts the Short and the Intermediate duration groups into two subgroups, the dim ones and the bright ones.
We describe two new open source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program for using Gaussian mixtures to do density estimation of noisy data using extreme deconvolution (XD) algorithms that has functionality not available in other XD tools. It allows the user to select between the AstroML (Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting methods and is compatible with scikit-learn machine learning algorithms (Pedregosa et al. 2011). Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model conditioned on known values of other parameters. EmpiriciSN is an example application of this functionality that can be used for fitting an XDGMM model to observed supernova/host datasets and predicting likely supernova parameters using on a model conditioned on observed host properties. It is primarily intended for simulating realistic supernovae for LSST data simulations based on empirical galaxy properties.
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