Cluster versus POTENT Density and Velocity Fields: Cluster Biasing and Omega

The density and velocity fields as extracted from the Abell/ACO clusters are compared to the corresponding fields recovered by the POTENT method from the Mark~III peculiar velocities of galaxies. In order to minimize non-linear effects and to deal with ill-sampled regions we smooth both fields using a Gaussian window with radii ranging between 12 - 20\hmpc. The density and velocity fields within 70\hmpc exhibit similarities, qualitatively consistent with gravitational instability theory and a linear biasing relation between clusters and mass. The random and systematic errors are evaluated with the help of mock catalogs. Quantitative comparisons within a volume containing $\sim\!12$ independent samples yield \betac\equiv\Omega^{0.6}/b_c=0.22\pm0.08, where $b_c$ is the cluster biasing parameter at 15\hmpc. If $b_c \sim 4.5$, as indicated by the cluster correlation function, our result is consistent with $\Omega \sim 1$.Comment: 18 pages, latex, 2 ps figures 6 gif figures. Accepted for pubblications in MNRA

Cosmological Parameters from Velocities, CMB and Supernovae

We compare and combine likelihood functions of the cosmological parameters Omega_m, h and sigma_8, from peculiar velocities, CMB and type Ia supernovae. These three data sets directly probe the mass in the Universe, without the need to relate the galaxy distribution to the underlying mass via a "biasing" relation. We include the recent results from the CMB experiments BOOMERANG and MAXIMA-1. Our analysis assumes a flat Lambda CDM cosmology with a scale-invariant adiabatic initial power spectrum and baryonic fraction as inferred from big-bang nucleosynthesis. We find that all three data sets agree well, overlapping significantly at the 2 sigma level. This therefore justifies a joint analysis, in which we find a joint best fit point and 95 per cent confidence limits of Omega_m=0.28 (0.17,0.39), h=0.74 (0.64,0.86), and sigma_8=1.17 (0.98,1.37). In terms of the natural parameter combinations for these data sigma_8 Omega_m^0.6 = 0.54 (0.40,0.73), Omega_m h = 0.21 (0.16,0.27). Also for the best fit point, Q_rms-ps = 19.7 muK and the age of the universe is 13.2 Gyr.Comment: 8 pages, 5 figures. Submitted to MNRA

Cluster versus POTENT density and velocity fields:cluster biasing and Omega

The density and velocity fields as extracted from the Abell/ACO clusters are compared with the corresponding fields recovered by the POTENT method from the Mark III peculiar velocities of galaxies. In order to minimize non-linear effects and to deal with ill-sampled regions, we smooth both fields using a Gaussian window with radii ranging between 12 and 20 h(-1) Mpc. The density and velocity fields within 70 h(-1) Mpc exhibit similarities, qualitatively consistent with gravitational instability theory and a linear biasing relation between clusters and mass. The random and systematic errors are evaluated with the help of mock catalogues. Quantitative comparisons within a volume containing similar to 12 independent samples yield beta(c)=Omega(0.6)/b(c)=0.22 +/- 0.08, where b(c) is the cluster biasing parameter at 15 h(-1) Mpc. If b(c)similar to 4.5, as indicated by the cluster correlation function, our result is consistent with Omega similar to 1

Large Scale Power Spectrum from Peculiar Velocities Via Likelihood Analysis

The power spectrum (PS) of mass density fluctuations, independent of `biasing', is estimated from the Mark III catalog of peculiar velocities using Bayesian statistics. A parametric model is assumed for the PS, and the free parameters are determined by maximizing the probability of the model given the data. The method has been tested using detailed mock catalogs. It has been applied to generalized CDM models with and without COBE normalization. The robust result for all the models is a relatively high PS, with $P(k) \Omega^{1.2} = (4.8 \pm 1.5) \times 10^3 (Mpc/h)^3$ at $k=0.1 h/Mpc$. An extrapolation to smaller scales using the different CDM models yields $\sigma_8 \Omega^{0.6} = 0.88 \pm 0.15$. The peak is weakly constrained to the range $0.02 \leq k \leq 0.06 h/Mpc$. These results are consistent with a direct computation of the PS (Kolatt & Dekel 1996). When compared to galaxy-density surveys, the implied values for $\beta$ ($\equiv \Omega^{0.6}/b$) are of order unity to within 25%. The parameters of the COBE-normalized, flat CDM model are confined by a 90% likelihood contour of the sort $\Omega h_{50}^\mu n^\nu = 0.8 \pm 0.2$, where $\mu = 1.3$ and $\nu = 3.4, 2.0$ for models with and without tensor fluctuations respectively. For open CDM the powers are $\mu = 0.95$ and $\nu = 1.4$ (no tensor fluctuations). A $\Gamma$-shape model free of COBE normalization yields only a weak constraint: $\Gamma = 0.4 \pm 0.2$.Comment: 19 pages, 8 figures, 2 tables. Accepted for publication in The Astrophysical Journa

Brief Announcement: Treewidth Modulator: Emergency Exit for DFVS

In the Directed Feedback Vertex Set (DFVS) problem, we are given as input a directed graph D and an integer k, and the objective is to check whether there exists a set S of at most k vertices such that F=D-S is a directed acyclic graph (DAG). Determining whether DFVS admits a polynomial kernel (parameterized by the solution size) is one of the most important open problems in parameterized complexity. In this article, we give a polynomial kernel for DFVS parameterized by the solution size plus the size of any treewidth-eta modulator, for any positive integer eta. We also give a polynomial kernel for the problem, which we call Vertex Deletion to treewidth-eta DAG, where given as input a directed graph D and a positive integer k, the objective is to decide whether there exists a set of at most k vertices, say S, such that D-S is a DAG and the treewidth of D-S is at most eta

Matrix Rigidity from the Viewpoint of Parameterized Complexity

The rigidity of a matrix A for a target rank r over a field F is the minimum Hamming distance between A and a matrix of rank at most r. Rigidity is a classical concept in Computational Complexity Theory: constructions of rigid matrices are known to imply lower bounds of significant importance relating to arithmetic circuits. Yet, from the viewpoint of Parameterized Complexity, the study of central properties of matrices in general, and of the rigidity of a matrix in particular, has been neglected. In this paper, we conduct a comprehensive study of different aspects of the computation of the rigidity of general matrices in the framework of Parameterized Complexity. Naturally, given parameters r and k, the Matrix Rigidity problem asks whether the rigidity of A for the target rank r is at most k. We show that in case F equals the reals or F is any finite field, this problem is fixed-parameter tractable with respect to k+r. To this end, we present a dimension reduction procedure, which may be a valuable primitive in future studies of problems of this nature. We also employ central tools in Real Algebraic Geometry, which are not well known in Parameterized Complexity, as a black box. In particular, we view the output of our dimension reduction procedure as an algebraic variety. Our main results are complemented by a W[1]-hardness result and a subexponential-time parameterized algorithm for a special case of Matrix Rigidity, highlighting the different flavors of this problem

Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA

We allow for nonlinear effects in the likelihood analysis of galaxy peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter Om and the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only Om as a free parameter. Since the likelihood is driven by the nonlinear regime, we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b. This allows for independent matching of the nonlinear behavior and an unbiased fit in the linear regime. The analysis assumes Gaussian fluctuations and errors, and a linear relation between velocity and density. Tests using proper mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from other data. The quoted 90% errors include cosmic variance. The improvement in likelihood due to the nonlinear correction is very significant for Mark3 and moderately so for SFI. When allowing deviations from LCDM, we find an indication for a wiggle in the power spectrum: an excess near k=0.05 and a deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the power spectrum from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows that the nonlinear procedure improves the goodness of fit and reduces a spatial gradient of concern in the linear analysis. The PCA allows addressing spatial features of the data and fine-tuning the theoretical and error models. It shows that the models used are appropriate for the cosmological parameter estimation performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001), improvements to text and figures, updated reference