Cosmological baryonic and matter densities from 600,000 SDSS Luminous Red Galaxies with photometric redshifts

We analyze MegaZ-LRG, a photometric-redshift catalogue of Luminous Red Galaxies (LRGs) based on the imaging data of the Sloan Digital Sky Survey (SDSS) 4th Data Release. MegaZ-LRG, presented in a companion paper, contains 10^6 photometric redshifts derived with ANNz, an Artificial Neural Network method, constrained by a spectroscopic sub-sample of 13,000 galaxies obtained by the 2dF-SDSS LRG and Quasar (2SLAQ) survey. The catalogue spans the redshift range 0.4 < z < 0.7 with an r.m.s. redshift error ~ 0.03(1+z), covering 5,914 deg^2 to map out a total cosmic volume 2.5 h^-3 Gpc^3. In this study we use the most reliable 600,000 photometric redshifts to present the first cosmological parameter fits to galaxy angular power spectra from a photometric redshift survey. Combining the redshift slices with appropriate covariances, we determine best-fitting values for the matter and baryon densities of Omega_m h = 0.195 +/- 0.023 and Omega_b/Omega_m = 0.16 +/- 0.036 (with the Hubble parameter h = 0.75 and scalar index of primordial fluctuations n = 1 held fixed). These results are in agreement with and independent of the latest studies of the Cosmic Microwave Background radiation, and their precision is comparable to analyses of contemporary spectroscopic-redshift surveys. We perform an extensive series of tests which conclude that our power spectrum measurements are robust against potential systematic photometric errors in the catalogue. We conclude that photometric-redshift surveys are competitive with spectroscopic surveys for measuring cosmological parameters in the simplest vanilla models. Future deep imaging surveys have great potential for further improvement, provided that systematic errors can be controlled.Comment: 24 pages, 23 figures, MNRAS accepte

Compressive Mining: Fast and Optimal Data Mining in the Compressed Domain

Real-world data typically contain repeated and periodic patterns. This suggests that they can be effectively represented and compressed using only a few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.). However, distance estimation when the data are represented using different sets of coefficients is still a largely unexplored area. This work studies the optimization problems related to obtaining the \emph{tightest} lower/upper bound on Euclidean distances when each data object is potentially compressed using a different set of orthonormal coefficients. Our technique leads to tighter distance estimates, which translates into more accurate search, learning and mining operations \textit{directly} in the compressed domain. We formulate the problem of estimating lower/upper distance bounds as an optimization problem. We establish the properties of optimal solutions, and leverage the theoretical analysis to develop a fast algorithm to obtain an \emph{exact} solution to the problem. The suggested solution provides the tightest estimation of the $L_2$-norm or the correlation. We show that typical data-analysis operations, such as k-NN search or k-Means clustering, can operate more accurately using the proposed compression and distance reconstruction technique. We compare it with many other prevalent compression and reconstruction techniques, including random projections and PCA-based techniques. We highlight a surprising result, namely that when the data are highly sparse in some basis, our technique may even outperform PCA-based compression. The contributions of this work are generic as our methodology is applicable to any sequential or high-dimensional data as well as to any orthogonal data transformation used for the underlying data compression scheme.Comment: 25 pages, 20 figures, accepted in VLD

Assessing a Hydrodynamic Description for Instabilities in Highly Dissipative, Freely Cooling Granular Gases

An intriguing phenomenon displayed by granular flows and predicted by kinetic-theory-based models is the instability known as particle "clustering," which refers to the tendency of dissipative grains to form transient, loose regions of relatively high concentration. In this work, we assess a modified-Sonine approximation recently proposed [Garz\'o et al., Physica A 376, 94 (2007)] for a granular gas via an examination of system stability. In particular, we determine the critical length scale associated with the onset of two types of instabilities -vortices and clusters- via stability analyses of the Navier-Stokes-order hydrodynamic equations by using the expressions of the transport coefficients obtained from both the standard and the modified-Sonine approximations. We examine the impact of both Sonine approximations over a range of solids fraction \phi <0.2 for small restitution coefficients e=0.25--0.4, where the standard and modified theories exhibit discrepancies. The theoretical predictions for the critical length scales are compared to molecular dynamics (MD) simulations, of which a small percentage were not considered due to inelastic collapse. Results show excellent quantitative agreement between MD and the modified-Sonine theory, while the standard theory loses accuracy for this highly dissipative parameter space. The modified theory also remedies a (highdissipation) qualitative mismatch between the standard theory and MD for the instability that forms more readily. Furthermore, the evolution of cluster size is briefly examined via MD, indicating that domain-size clusters may remain stable or halve in size, depending on system parameters.Comment: 4 figures; to be published in Phys. Rev.

Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data

Bathymetric data depicts the geomorphology of the seabottom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor topography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially different physiographic characteristics. In the present paper histograms of biologically motivated invariant image attributes are used for characterization of local geomorphological feahires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clusters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Machine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA