30 research outputs found
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 CDM simulations
in boxes of 128, 256, 512 Mpc aside, for a total of 24, differing just
by the initial seeds. Among the simulations with 128 and 512 Mpc aside,
we suitably select those closest and farthest from {\it average}. Numerical and
linear spectra are suitably connected at low so to evaluate the
effects of sample variance on shear spectra for 5 or 10
tomographic bands. We find that shear spectra obtained by using 128 Mpc
simulations can vary up to , just because of the seed. Sample
variance lowers to , when using 512 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
precision expected for data, if still using 8 boxes, we require a size -- Mpc for them.Comment: accepted by Ap
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
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 CDM scenario, scales necessarily as
. 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 level, some
deviations from the CDM model are found at low redshifts, which may be
associated with the current tension between local and global determinations of
.Comment: 15 pages, 12 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 () based on Cepheids e Type Ia
supernova differ by from the estimated value of from
Planck CMB observations under CDM assumptions. In order to better
understand this 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
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