Spectra of dynamical Dark Energy cosmologies from constant-w models

WMAP5 and related data have greatly restricted the range of acceptable cosmologies, by providing precise likelihood ellypses on the the w_0-w_a plane. We discuss first how such ellypses can be numerically rebuilt, and present then a map of constant-w models whose spectra, at various redshift, are expected to coincide with acceptable models within ~1%

Sample variance in N--body simulations and impact on tomographic shear predictions

We study the effects of sample variance in N--body simulations, as a function of the size of the simulation box, namely in connection with predictions on tomographic shear spectra. We make use of a set of 8 $\Lambda$CDM simulations in boxes of 128, 256, 512 $h^{-1}$Mpc aside, for a total of 24, differing just by the initial seeds. Among the simulations with 128 and 512 $h^{-1}$Mpc aside, we suitably select those closest and farthest from {\it average}. Numerical and linear spectra $P(k,z)$ are suitably connected at low $k$ so to evaluate the effects of sample variance on shear spectra $C_{ij}(\ell)$ for 5 or 10 tomographic bands. We find that shear spectra obtained by using 128 $h^{-1}$Mpc simulations can vary up to $\sim 25\, \%$, just because of the seed. Sample variance lowers to $\sim 3.3\, \%$, when using 512 $h^{-1}$Mpc. These very percentages could however slightly vary, if other sets of the same number of realizations were considered. Accordingly, in order to match the $\sim 1\, \%$ precision expected for data, if still using 8 boxes, we require a size $\sim 1300$ --$1700 \, h^{-1}$ Mpc for them.Comment: accepted by Ap

Null test for interactions in the dark sector

Since there is no known symmetry in Nature that prevents a non-minimal coupling between the dark energy (DE) and cold dark matter (CDM) components, such a possibility constitutes an alternative to standard cosmology, with its theoretical and observational consequences being of great interest. In this paper we propose a new null test on the standard evolution of the dark sector based on the time dependence of the ratio between the CDM and DE energy densities which, in the standard $\Lambda$CDM scenario, scales necessarily as $a^{-3}$. We use the latest measurements of type Ia supernovae, cosmic chronometers and angular baryonic acoustic oscillations to reconstruct the expansion history using model-independent Machine Learning techniques, namely, the Linear Model formalism and Gaussian Processes. We find that while the standard evolution is consistent with the data at $3\sigma$ level, some deviations from the $\Lambda$CDM model are found at low redshifts, which may be associated with the current tension between local and global determinations of $H_0$.Comment: 15 pages, 12 figure

Dark MaGICC: the effect of Dark Energy on galaxy formation. Cosmology does matter

We present the Dark MaGICC project, which aims to investigate the effect of Dark Energy (DE) modeling on galaxy formation via hydrodynamical cosmological simulations. Dark MaGICC includes four dynamical Dark Energy scenarios with time varying equations of state, one with a self-interacting Ratra-Peebles model. In each scenario we simulate three galaxies with high resolution using smoothed particle hydrodynamics (SPH). The baryonic physics model is the same used in the Making Galaxies in a Cosmological Context (MaGICC) project, and we varied only the background cosmology. We find that the Dark Energy parameterization has a surprisingly important impact on galaxy evolution and on structural properties of galaxies at z=0, in striking contrast with predictions from pure Nbody simulations. The different background evolutions can (depending on the behavior of the DE equation of state) either enhance or quench star formation with respect to a LCDM model, at a level similar to the variation of the stellar feedback parameterization, with strong effects on the final galaxy rotation curves. While overall stellar feedback is still the driving force in shaping galaxies, we show that the effect of the Dark Energy parameterization plays a larger role than previously thought, especially at lower redshifts. For this reason, the influence of Dark Energy parametrization on galaxy formation must be taken into account, especially in the era of precision cosmology.Comment: 11 pages, 13 figure

Bouncing solutions in Rastall's theory with a barotropic fluid

Rastall's theory is a modification of Einstein's theory of gravity where the covariant divergence of the stress-energy tensor is no more vanishing, but proportional to the gradient of the Ricci scalar. The motivation of this theory is to investigate a possible non-minimal coupling of the matter fields to geometry which, being proportional to the curvature scalar, may represent an effective description of quantum gravity effects. Non-conservation of the stress-energy tensor, via Bianchi identities, implies new field equations which have been recently used in a cosmological context, leading to some interesting results. In this paper we adopt Rastall's theory to reproduce some features of the effective Friedmann's equation emerging from loop quantum cosmology. We determine a class of bouncing cosmological solutions and comment about the possibility of employing these models as effective descriptions of the full quantum theory.Comment: Latex file, 14 pages, 1 figure in eps format. Typos corrected, one reference added. Published versio

Machine Learning the Hubble Constant

Local measurements of the Hubble constant ($H_0$) based on Cepheids e Type Ia supernova differ by $\approx 5 \sigma$ from the estimated value of $H_0$ from Planck CMB observations under $\Lambda$CDM assumptions. In order to better understand this $H_0$ tension, the comparison of different methods of analysis will be fundamental to interpret the data sets provided by the next generation of surveys. In this paper, we deploy machine learning algorithms to measure the $H_0$ through a regression analysis on synthetic data of the expansion rate assuming different values of redshift and different levels of uncertainty. We compare the performance of different algorithms as Extra-Trees, Artificial Neural Network, Extreme Gradient Boosting, Support Vector Machines, and we find that the Support Vector Machine exhibits the best performance in terms of bias-variance tradeoff, showing itself a competitive cross-check to non-supervised regression methods such as Gaussian Processes.Comment: 13 pages, 3 figures. Comments welcome. Scripts available at https://github.com/astrobengaly/machine_learning_H