The Variance of QSO Counts in Cells

{}From three quasar samples with a total of 1038 objects in the redshift range $1.0 \div 2.2$ we measure the variance $\sigma^2$ of counts in cells of volume $V_u$. By a maximum likelihood analysis applied separately on these samples we obtain estimates of $\sigma^2(\ell)$, with $\ell \equiv V_u^{1/3}$. The analysis from a single catalog for $\ell = ~40~h^{-1}$ Mpc and from a suitable average over the three catalogs for $\ell = ~60,~80$ and $100~h^{-1}$ Mpc, gives $\sigma^2(\ell) = 0.46^{+0.27}_{-0.27}$, $0.18^{+0.14}_{-0.15}$, $0.05^{+0.14}_{-0.05}$ and $0.12^{+0.13}_{-0.12}$, respectively, where the $70\%$ confidence ranges account for both sampling errors and statistical fluctuations in the counts. This allows a comparison of QSO clustering on large scales with analogous data recently obtained both for optical and IRAS galaxies: QSOs seem to be more clustered than these galaxies by a biasing factor $b_{QSO}/b_{gal} \sim 1.4 - 2.3$.Comment: 13 pages in plain Tex, 5 figures available in postscript in a separate file, submitted to ApJ, DAPD-33

Halo-model Analysis of the Clustering of Photometrically Selected Galaxies from SDSS

We measure the angular 2-point correlation functions of galaxies in a volume limited, photometrically selected galaxy sample from the fifth data release of the Sloan Digital Sky Survey. We split the sample both by luminosity and galaxy type and use a halo-model analysis to find halo-occupation distributions that can simultaneously model the clustering of all, early-, and late-type galaxies in a given sample. Our results for the full galaxy sample are generally consistent with previous results using the SDSS spectroscopic sample, taking the differences between the median redshifts of the photometric and spectroscopic samples into account. We find that our early- and late- type measurements cannot be fit by a model that allows early- and late-type galaxies to be well-mixed within halos. Instead, we introduce a new model that segregates early- and late-type galaxies into separate halos to the maximum allowed extent. We determine that, in all cases, it provides a good fit to our data and thus provides a new statistical description of the manner in which early- and late-type galaxies occupy halos.Comment: Accepted to MNRAS 11 pages, 6 figure

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

The Imperial IRAS-FSC Redshift Catalogue: luminosity functions, evolution and galaxy bias

We present the luminosity function and selection function of 60 micron galaxies selected from the Imperial IRAS-FSC Redshift Catalogue (IIFSCz). Three methods, including the 1/Vmax} and the parametric and non-parametric maximum likelihood estimator, are used and results agree well with each other. A density evolution proportional to (1+z)^3.4 or a luminosity evolution exp(1.7 t_L / \tau)$ where t_L is the look-back time is detected in the full sample in the redshift range [0.02, 0.1], consistent with previous analyses. Of the four infrared subpopulations, cirrus-type galaxies and M82-type starbursts show similar evolutionary trends, galaxies with significant AGN contributions show stronger positive evolution and Arp 220-type starbursts exhibit strong negative evolution. The dominant subpopulation changes from cirrus-type galaxies to M82-type starbursts at log (L_60 / L_Sun) ~ 10.3. In the second half of the paper, we derive the projected two-point spatial correlation function for galaxies of different infrared template type. The mean relative bias between cirrus-type galaxies and M82-type starbursts, which correspond to quiescent galaxies with optically thin interstellar dust and actively star-forming galaxies respectively, is calculated to be around 1.25. The relation between current star formation rate (SFR) in star-forming galaxies and environment is investigated by looking at the the dependence of clustering on infrared luminosity. We found that M82-type actively star-forming galaxies show stronger clustering as infrared luminosity / SFR increases. The correlation between clustering strength and SFR in the local Universe seems to echo the basic trend seen in star-forming galaxies in the Great Observatories Origins Deep Survey (GOODS) fields at z ~ 1.Comment: 15 pages, 11 figures, accepted for publication in MNRA

Continuous Fields and Discrete Samples: Reconstruction through Delaunay Tessellations

Here we introduce the Delaunay Density Estimator Method. Its purpose is rendering a fully volume-covering reconstruction of a density field from a set of discrete data points sampling this field. Reconstructing density or intensity fields from a set of irregularly sampled data is a recurring key issue in operations on astronomical data sets, both in an observational context as well as in the context of numerical simulations. Our technique is based upon the stochastic geometric concept of the Delaunay tessellation generated by the point set. We shortly describe the method, and illustrate its virtues by means of an application to an N-body simulation of cosmic structure formation. The presented technique is a fully adaptive method: automatically it probes high density regions at maximum possible resolution, while low density regions are recovered as moderately varying regions devoid of the often irritating shot-noise effects. Of equal importance is its capability to sharply and undilutedly recover anisotropic density features like filaments and walls. The prominence of such features at a range of resolution levels within a hierarchical clustering scenario as the example of the standard CDM scenario is shown to be impressively recovered by our scheme.Comment: 4 pages, 2 figures, accepted for publication in Astronomy & Astrophysics Letter

Preferential concentration of inertial sub-kolmogorov particles. The roles of mass loading of particles, Stokes and Reynolds numbers

Turbulent flows laden with inertial particles present multiple open questions and are a subject of great interest in current research. Due to their higher density compared to the carrier fluid, inertial particles tend to form high concentration regions, i.e. clusters, and low concentration regions, i.e. voids, due to the interaction with the turbulence. In this work, we present an experimental investigation of the clustering phenomenon of heavy sub-Kolmogorov particles in homogeneous isotropic turbulent flows. Three control parameters have been varied over significant ranges: $Re_{\lambda} \in [170 - 450]$, $St\in [0.1 - 5]$ and volume fraction $\phi_v\in [2\times 10^{-6} - 2\times 10^{-5}]$. The scaling of clustering characteristics, such as the distribution of Vorono\"i areas and the dimensions of cluster and void regions, with the three parameters are discussed. In particular, for the polydispersed size distributions considered here, clustering is found to be enhanced strongly (quasi-linearly) by $Re_{\lambda}$ and noticeably (with a square-root dependency) with $\phi_v$, while the cluster and void sizes, scaled with the Kolmogorov lengthscale $\eta$, are driven primarily by $Re_{\lambda}$. Cluster length $\sqrt{\langle A_c \rangle}$ scales up to $\approx 100 {\eta}$, measured at the highest $Re_{\lambda}$, while void length $\sqrt{\langle A_v \rangle}$ scaled also with $\eta$ is typically two times larger ($\approx 200 {\eta}$). The lack of sensitivity of the above characteristics to the Stokes number lends support to the "sweep-stick" particle accumulation scenario. The non-negligible influence of the volume fraction, however, is not considered by that model and can be connected with collective effects