We present simulations of the cosmic microwave background radiation (CMBR) power spectrum for a class of mixed, non-Gaussian, primordial random fields. We assume a skew-positive mixed model with adiabatic inflation perturbations plus additional isocurvature perturbations possibly produced by topological defects. The joint probability distribution used in this context is a weighted combination of Gaussian and non-Gaussian random fields, such as Pð Þ ¼ ð1 À Þ f 1 ð Þ þ f 2 ð Þ, where f 1 ð Þ is a Gaussian distribution, f 2 ð Þ is a non-Gaussian general distribution, and is a scale-dependent mixture parameter. Results from simulations of CMBR temperature and polarization power spectra show a distinct signature for very small deviations (P0.1%) from a pure Gaussian field. We discuss the main properties of such mixed models, as well as their predictions, and suggestions on how to apply them to small-scale CMBR observations. A reduced 2 test shows that the contribution of an isocurvature fluctuation field is not ruled out in actual CMBR observations, even in the Wilkinson Microwave Anisotropy Probe first-year sky map.

This work presents a reduced χ 2 ν test to search for non-gaussian signals in the CMBR TT power spectrum of recent CMBR data, WMAP, ACBAR and CBI data sets, assuming a mixed density field including adiabatic and isocurvature fluctuations. We assume a skew positive mixed model with adiabatic inflation perturbations plus additional isocurvature perturbations possibly produced by topological defects. The joint probability distribution used in this context is a weighted combination of Gaussian and non-Gaussian random fields. Results from simulations of CMBR temperature for the mixed field show a distinct signature in CMB power spectrum for very small deviations (∼ 0.1%) from a pure Gaussian field, and can be used as a direct test for the nature of primordial fluctuations. A reduced χ 2 ν test applied on the most recent CMBR observations reveals that an isocurvature fluctuations field is not ruled out and indeed permits a very good description for a flat geometry Λ-CDM universe, χ

This paper presents the preliminary results of the characterization of pattern evolution in the process of cosmic structure formation. We are applying on N-body cosmological simulations data the technique proposed by Rosa, Sharma & Valdivia (1999) and Ramos et al. (2000) to estimate the time behavior of asymmetries in the gradient field. The gradient pattern analysis is a well tested tool, used to build asymmetrical fragmentation parameters estimated over a gradient field of an image matrix able to quantify a complexity measure of nonlinear extended systems. In this investigation we work with the high resolution cosmological data simulated by the Virgo consortium, in different time steps, in order to obtain a diagnostic of the spatio-temporal disorder in the matter density field. We perform the calculations of the gradient vectors statistics, such as mean, variance, skewness, kurtosis, and correlations on the gradient field. Our main goal is to determine different dynamical regimes through the analysis of complex patterns arising from the evolutionary process of structure formation. The results show that the gradient pattern technique, specially the statistical analysis of second and third gradient moment, may represent a very useful tool to describe the matter clustering in the Universe.

Context. One possible way to investigate the nature of the primordial power spectrum fluctuations is by investigating the statistical properties of the local maximum in the density fluctuation fields.Aims. In this work we present a study of the mean correlation function, ξ r , and the correlation function for high-amplitude fluctuations (peak-peak correlation) in a slighlty non-Gaussian context. Methods. From the definition of the correlation excess, we computed the Gaussian two-point correlation function and, using an expansion in generalized Hermite polynomials, we estimated the correlation of high-density peaks in a non-Gaussian field with a generic distribution and power spectrum. We also applied the results to a scale-mixed distribution model, which corresponds to a nearly Gaussian model. Results. The results reveal that, even for a small deviation from Gaussianity, we can expect high-density peaks to be much more correlated than in a Gaussian field with the same power spectrum. In addition, the calculations reveal how the amplitude of the peaks in the fluctuation field is related to the existing correlations. Conclusions. Our results may be used as an additional tool for investigating the behavior of the N-point correlation function, to understand how non-Gaussian correlations affect the peak-peak statistics, and extract more information about the statistics of the density field.

Context. We investigate the behavior of the mass variance and the mass function of galaxy clusters in a mixed distribution model. Aims. Our aim is to find a relation between the mass variance at a 8 h −1 Mpc scale, σ 8 , and the parameter controlling the Gaussian deviation in the model, α 0 , and to constrain the non-Gaussianity using observational data at cluster scales. Methods. By assuming that the statistics of the density field is built as a weighted mixture of two components, a Gaussian + Lognormal distribution, we rewrite the mass variance expression and the mass function for galaxy clusters. Results. We find a relation between the mass variance at a 8 h −1 Mpc scale, σ 8 , and the scale parameter controlling the Gaussian deviation in the model, α 0 . This result, in conjunction with observational constraints on the mass variance and high-z galaxy clustering, suggests a scenario where structures develop earlier in comparison to strictly Gaussian models, even for α 0 0.003 Mpc. Our model also indicates that only well selected galaxy cluster samples at z 1 can discriminate between Gaussian and non-Gaussian (mixed) distribution models.