A late-time transition in the equation of state versus Lambda-CDM

We study a model of the dark energy which exhibits a rapid change in its equation of state w(z), such as occurs in vacuum metamorphosis. We compare the model predictions with CMB, large scale structure and supernova data and show that a late-time transition is marginally preferred over standard Lambda-CDM.Comment: 4 pages, 1 figure, to appear in the proceedings of XXXVIIth Rencontres de Moriond, "The Cosmological Model", March 200

Restoring the sting to metric preheating

The relative growth of field and metric perturbations during preheating is sensitive to initial conditions set in the preceding inflationary phase. Recent work suggests this may protect super-Hubble metric perturbations from resonant amplification during preheating. We show that this possibility is fragile and sensitive to the specific form of the interactions between the inflaton and other fields. The suppression is naturally absent in two classes of preheating in which either (1) the vacua of the non-inflaton fields during inflation are deformed away from the origin, or (2) the effective masses of non-inflaton fields during inflation are small but during preheating are large. Unlike the simple toy model of a $g^2 \phi^2 \chi^2$ coupling, most realistic particle physics models contain these other features. Moreover, they generically lead to both adiabatic and isocurvature modes and non-Gaussian scars on super-Hubble scales. Large-scale coherent magnetic fields may also appear naturally.Comment: 6 pages, 3 ps figures, RevTex, revised discussion of backreaction and new figure. To appear Phys. Rev. D (Rapid Communication

S-band omnidirectional antenna for the SERT-C satellite

The program to design an S-band omnidirectional antenna system for the SERT-C spacecraft is discussed. The program involved the tasks of antenna analyses by computer techniques, scale model radiation pattern measurements of a number of antenna systems, full-scale RF measurements, and the recommended design, including detailed drawings. A number of antenna elements were considered: the cavity-backed spiral, quadrifilar helix, and crossed-dipoles were chosen for in-depth studies. The final design consisted of a two-element array of cavity-backed spirals mounted on opposite sides of spacecraft and fed in-phase through a hybrid junction. This antenna system meets the coverage requirement of having a gain of at least minus 10 dBi over 50 percent of a 4 pi steradian sphere with the solar panels in operation. This coverage level is increased if the ground station has the capability to change polarization

A late-time transition in the cosmic dark energy?

We study constraints from the latest CMB, large scale structure (2dF, Abell/ACO, PSCz) and SN1a data on dark energy models with a sharp transition in their equation of state, w(z). Such a transition is motivated by models like vacuum metamorphosis where non-perturbative quantum effects are important at late times. We allow the transition to occur at a specific redshift, z_t, to a final negative pressure -1 < w_f < -1/3. We find that the CMB and supernovae data, in particular, prefer a late-time transition due to the associated delay in cosmic acceleration. The best fits (with 1 sigma errors) to all the data are z_t = 2.0^{+2.2}_{-0.76}, \Omega_Q = 0.73^{+0.02}_{-0.04} and w_f = -1^{+0.2}.Comment: 6 Pages, 5 colour figures, MNRAS styl

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Resolving structural variability in network models and the brain

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar diagnostics presented in statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling---in addition to several summary statistics, including the mean clustering coefficient, shortest path length, and network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be embedded in anatomical brain regions tend to produce distributions that are similar to those extracted from the brain. We also find that network models hardcoded to display one network property do not in general also display a second, suggesting that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.Comment: 24 pages, 11 figures, 1 table, supplementary material

Adiabatic Gravitational Perturbation During Reheating

We study the possibilities of parametric amplification of the gravitational perturbation during reheating in single-field inflation models. Our result shows that there is no additional growth of the super-horizon modes beyond the usual predictions.Comment: Refs added; New version to appear in PR

Bayesian estimation applied to multiple species

Observed data are often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian estimation applied to multiple species) which significantly improves on the standard maximum likelihood approach in the case where the probability for each data point being “pure” is known. We discuss the application of BEAMS to future type-Ia supernovae (SNIa) surveys, such as LSST, which are projected to deliver over a million supernovae light curves without spectra. The multiband light curves for each candidate will provide a probability of being Ia (pure) but the full sample will be significantly contaminated with other types of supernovae and transients. Given a sample of N supernovae with mean probability, ⟨P⟩, of being Ia, BEAMS delivers parameter constraints equal to N⟨P⟩ spectroscopically confirmed SNIa. In addition BEAMS can be simultaneously used to tease apart different families of data and to recover properties of the underlying distributions of those families (e.g. the type-Ibc and II distributions). Hence BEAMS provides a unified classification and parameter estimation methodology which may be useful in a diverse range of problems such as photometric redshift estimation or, indeed, any parameter estimation problem where contamination is an issue