Constraining dark energy fluctuations with supernova correlations

We investigate constraints on dark energy fluctuations using type Ia supernovae. If dark energy is not in the form of a cosmological constant, that is if the equation of state is not equal to -1, we expect not only temporal, but also spatial variations in the energy density. Such fluctuations would cause local variations in the universal expansion rate and directional dependences in the redshift-distance relation. We present a scheme for relating a power spectrum of dark energy fluctuations to an angular covariance function of standard candle magnitude fluctuations. The predictions for a phenomenological model of dark energy fluctuations are compared to observational data in the form of the measured angular covariance of Hubble diagram magnitude residuals for type Ia supernovae in the Union2 compilation. The observational result is consistent with zero dark energy fluctuations. However, due to the limitations in statistics, current data still allow for quite general dark energy fluctuations as long as they are in the linear regime.Comment: 18 pages, 6 figures, matches the published versio

Large Scale Structure Formation with Global Topological Defects. A new Formalism and its implementation by numerical simulations

We investigate cosmological structure formation seeded by topological defects which may form during a phase transition in the early universe. First we derive a partially new, local and gauge invariant system of perturbation equations to treat microwave background and dark matter fluctuations induced by topological defects or any other type of seeds. We then show that this system is well suited for numerical analysis of structure formation by applying it to seeds induced by fluctuations of a global scalar field. Our numerical results are complementary to previous investigations since we use substantially different methods. The resulting microwave background fluctuations are compatible with older simulations. We also obtain a scale invariant spectrum of fluctuations with about the same amplitude. However, our dark matter results yield a smaller bias parameter compatible with $b\sim 2$ on a scale of $20 Mpc$ in contrast to previous work which yielded to large bias factors. Our conclusions are thus more positive. According to the aspects analyzed in this work, global topological defect induced fluctuations yield viable scenarios of structure formation and do better than standard CDM on large scales.Comment: uuencoded, compressed tar-file containing the text in LaTeX and 12 Postscript Figures, 41 page

Searching for a Cosmological Preferred Axis: Union2 Data Analysis and Comparison with Other Probes

We review, compare and extend recent studies searching for evidence for a preferred cosmological axis. We start from the Union2 SnIa dataset and use the hemisphere comparison method to search for a preferred axis in the data. We find that the hemisphere of maximum accelerating expansion rate is in the direction $(l,b)=({309^\circ}^{+23^\circ}_{-3^\circ}, {18^\circ}^{+11^\circ}_{-10^\circ})$ (\omm=0.19) while the hemisphere of minimum acceleration is in the opposite direction $(l,b)=({129^\circ}^{+23^\circ}_{-3^\circ},{-18^\circ}^{+10^\circ}_{-11^\circ})$ (\omm=0.30). The level of anisotropy is described by the normalized difference of the best fit values of \omm between the two hemispheres in the context of \lcdm fits. We find a maximum anisotropy level in the Union2 data of \frac{\Delta \ommax}{\bomm}=0.43\pm 0.06. Such a level does not necessarily correspond to statistically significant anisotropy because it is reproduced by about $30$ of simulated isotropic data mimicking the best fit Union2 dataset. However, when combined with the axes directions of other cosmological observations (bulk velocity flow axis, three axes of CMB low multipole moments and quasar optical polarization alignment axis), the statistical evidence for a cosmological anisotropy increases dramatically. We estimate the probability that the above independent six axes directions would be so close in the sky to be less than $1$. Thus either the relative coincidence of these six axes is a very large statistical fluctuation or there is an underlying physical or systematic reason that leads to their correlation.Comment: 10 pages, 7 figures. Accepted in JCAP (to appear). Extended analysis with redshift tomography of SnIa, included errorbars and increased number of axes. The Mathematica 7 files with the data used for the production of the figures along with a Powerpoint file with additional figures may be downloaded from http://leandros.physics.uoi.gr/anisotrop

Cold Plus Hot Dark Matter Cosmology in the Light of Solar and Atmospheric Neutrino Oscillations

We explore the implications of possible neutrino oscillations, as indicated by the solar and atmospheric neutrino experiments, for the cold plus hot dark matter scenario of large scale structure formation. We find that there are essentially three distinct schemes that can accommodate the oscillation data and which also allow for dark matter neutrinos. These include (i) three nearly degenerate (in mass) neutrinos, (ii) non-degenerate masses with $\nu_\tau$ in the eV range, and (iii) nearly degenerate $\nu_\mu-\nu_\tau$ pair (in the eV range), with the additional possibility that the electron neutrino is cosmologically significant. The last two schemes invoke a `sterile' neutrino which is light (< or ~ eV). We discuss the implications of these schemes for $\bar{\nu}_\mu - \bar{\nu}_e$ and $\nu_\mu - \nu_\tau$ oscillation, and find that scheme (ii) in particular, predicts them to be in the observable range. As far as structure formation is concerned, we compare the one neutrino flavor case with a variety of other possibilities, including two and three degenerate neutrino flavors. We show, both analytically and numerically, the effects of these neutrino mass scenarios on the amplitude of cosmological density fluctuations. With a Hubble constant of 50 km s$^{-1}$ Mpc$^{-1}$, a spectral index of unity, and $\Omega_{baryon} = 0.05$, the two and three flavor scenarios fit the observational data marginally better than the single flavor scheme. However, taking account of the uncertainties in these parameters, we show that it is premature to pick a clear winner.Comment: 1 LaTEX file plus 1 uuencoded Z-compressed tar file with 3 postscript figure

Tension between SN and BAO: current status and future forecasts

Using real and synthetic Type Ia SNe (SNeIa) and baryon acoustic oscillations (BAO) data representing current observations forecasts, this paper investigates the tension between those probes in the dark energy equation of state (EoS) reconstruction considering the well known CPL model and Wang's low correlation reformulation. In particular, here we present simulations of BAO data from both the the radial and transverse directions. We also explore the influence of priors on Omega_m and Omega_b on the tension issue, by considering 1-sigma deviations in either one or both of them. Our results indicate that for some priors there is no tension between a single dataset (either SNeIa or BAO) and their combination (SNeIa+BAO). Our criterion to discern the existence of tension (sigma-distance) is also useful to establish which is the dataset with most constraining power; in this respect SNeIa and BAO data switch roles when current and future data are considered, as forecasts predict and spectacular quality improvement on BAO data. We also find that the results on the tension are blind to the way the CPL model is addressed: there is a perfect match between the original formulation and that by the low correlation optimized, but the errors on the parameters are much narrower in all cases of our exhaustive exploration, thus serving the purpose of stressing the convenience of this reparametrization.Comment: 21 pages, under review in JCA

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine