Studying the properties of galaxy cluster morphology estimators

X-ray observations of galaxy clusters reveal a large range of morphologies with various degrees of disturbance, showing that the assumptions of hydrostatic equilibrium and spherical shape which are used to determine the cluster mass from X-ray data are not always satisfied. It is therefore important for the understanding of cluster properties as well as for cosmological applications to detect and quantify substructure in X-ray images of galaxy clusters. Two promising methods to do so are power ratios and center shifts. Since these estimators can be heavily affected by Poisson noise and X-ray background, we performed an extensive analysis of their statistical properties using a large sample of simulated X-ray observations of clusters from hydrodynamical simulations. We quantify the measurement bias and error in detail and give ranges where morphological analysis is feasible. A new, computationally fast method to correct for the Poisson bias and the X-ray background contribution in power ratio and center shift measurements is presented and tested for typical XMM-Newton observational data sets. We studied the morphology of 121 simulated cluster images and establish structure boundaries to divide samples into relaxed, mildly disturbed and disturbed clusters. In addition, we present a new morphology estimator - the peak of the 0.3-1 r500 P3/P0 profile to better identify merging clusters. The analysis methods were applied to a sample of 80 galaxy clusters observed with XMM-Newton. We give structure parameters (P3/P0 in r500, w and P3/P0_max) for all 80 observed clusters. Using our definition of the P3/P0 (w) substructure boundary, we find 41% (47%) of our observed clusters to be disturbed.Comment: Replaced to match version published in A&A, Eq. 1 correcte

Hashing with binary autoencoders

An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image from the binary code produced by the hash function. We show that the optimization can be simplified with the method of auxiliary coordinates. This reformulates the optimization as alternating two easier steps: one that learns the encoder and decoder separately, and one that optimizes the code for each image. Image retrieval experiments, using precision/recall and a measure of code utilization, show the resulting hash function outperforms or is competitive with state-of-the-art methods for binary hashing.Comment: 22 pages, 11 figure

Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to perform a family of nonconvex surrogates of $L_0$-norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then we propose to solve the problem by Iteratively Reweighted Nuclear Norm (IRNN) algorithm. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms

Improved variable selection with Forward-Lasso adaptive shrinkage

Recently, considerable interest has focused on variable selection methods in regression situations where the number of predictors, $p$, is large relative to the number of observations, $n$. Two commonly applied variable selection approaches are the Lasso, which computes highly shrunk regression coefficients, and Forward Selection, which uses no shrinkage. We propose a new approach, "Forward-Lasso Adaptive SHrinkage" (FLASH), which includes the Lasso and Forward Selection as special cases, and can be used in both the linear regression and the Generalized Linear Model domains. As with the Lasso and Forward Selection, FLASH iteratively adds one variable to the model in a hierarchical fashion but, unlike these methods, at each step adjusts the level of shrinkage so as to optimize the selection of the next variable. We first present FLASH in the linear regression setting and show that it can be fitted using a variant of the computationally efficient LARS algorithm. Then, we extend FLASH to the GLM domain and demonstrate, through numerous simulations and real world data sets, as well as some theoretical analysis, that FLASH generally outperforms many competing approaches.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS375 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The cluster Abell 780: an optical view

The Abell 780 cluster, better known as the Hydra A cluster, has been thouroughly analyzed in X-rays. However, little is known on its optical properties. We derive the galaxy luminosity function (GLF) in this apparently relaxed cluster, and search for possible environmental effects by comparing the GLFs in various regions, and by looking at the galaxy distribution at large scale around Abell 780. Our study is based on optical images obtained with the ESO 2.2m telescope and WFI camera in the B and R bands, covering a total region of 67.22x32.94 arcmin^2, or 4.235x2.075 Mpc^2 for a cluster redshift of 0.0539. In a region of 500 kpc radius around the cluster centre, the GLF in the R band shows a double structure, with a broad and flat bright part and a flat faint end that can be fit by a power law with an index alpha=-0.85+-0.12 in the 20.25<R<21.75 interval. If we divide this 500 kpc radius region in North+South or East+West halves, we find no clear difference between the GLFs in these smaller regions. No obvious large scale structure is apparent within 5 Mpc from the cluster, based on galaxy redshifts and magnitudes collected from the NED database in a much larger region than that covered by our data, suggesting that there is no major infall of material in any preferential direction. However, the Serna-Gerbal method reveals the presence of a gravitationally bound structure of 27 galaxies, which includes the cD, and of a more strongly gravitationally bound structure of 14 galaxies. These optical results agree with the overall relaxed structure of Abell 780 previously derived from X-ray analyses.Comment: Accepted for publication in Astronomy & Astrophysic

Generalized Nonconvex Nonsmooth Low-Rank Minimization

As surrogate functions of $L_0$-norm, many nonconvex penalty functions have been proposed to enhance the sparse vector recovery. It is easy to extend these nonconvex penalty functions on singular values of a matrix to enhance low-rank matrix recovery. However, different from convex optimization, solving the nonconvex low-rank minimization problem is much more challenging than the nonconvex sparse minimization problem. We observe that all the existing nonconvex penalty functions are concave and monotonically increasing on $[0,\infty)$. Thus their gradients are decreasing functions. Based on this property, we propose an Iteratively Reweighted Nuclear Norm (IRNN) algorithm to solve the nonconvex nonsmooth low-rank minimization problem. IRNN iteratively solves a Weighted Singular Value Thresholding (WSVT) problem. By setting the weight vector as the gradient of the concave penalty function, the WSVT problem has a closed form solution. In theory, we prove that IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthetic data and real images demonstrate that IRNN enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms.Comment: IEEE International Conference on Computer Vision and Pattern Recognition, 201