Neutrino oscillations in a stochastic model for space-time foam

We study decoherence models for flavour oscillations in four-dimensional stochastically fluctuating space times and discuss briefly the sensitivity of current neutrino experiments to such models. We pay emphasis on demonstrating the model dependence of the associated decoherence-induced damping coefficients in front of the oscillatory terms in the respective transition probabilities between flavours. Within the context of specific models of foam, involving point-like D-branes and leading to decoherence-induced damping which is inversely proportional to the neutrino energies, we also argue that future limits on the relevant decoherence parameters coming from TeV astrophysical neutrinos, to be observed in ICE-CUBE, are not far from theoretically expected values with Planck mass suppression. Ultra high energy neutrinos from Gamma Ray Bursts at cosmological distances can also exhibit in principle sensitivity to such effects.Comment: 12 pages RevTex4, no figure

Stochastic Gravity: Theory and Applications

Whereas semiclassical gravity is based on the semiclassical Einstein equation with sources given by the expectation value of the stress-energy tensor of quantum fields, stochastic semiclassical gravity is based on the Einstein-Langevin equation, which has in addition sources due to the noise kernel.In the first part, we describe the fundamentals of this new theory via two approaches: the axiomatic and the functional. In the second part, we describe three applications of stochastic gravity theory. First, we consider metric perturbations in a Minkowski spacetime: we compute the two-point correlation functions for the linearized Einstein tensor and for the metric perturbations. Second, we discuss structure formation from the stochastic gravity viewpoint. Third, we discuss the backreaction of Hawking radiation in the gravitational background of a quasi-static black hole.Comment: 75 pages, no figures, submitted to Living Reviews in Relativit

Non-linear inflationary perturbations

We present a method by which cosmological perturbations can be quantitatively studied in single and multi-field inflationary models beyond linear perturbation theory. A non-linear generalization of the gauge-invariant Sasaki-Mukhanov variables is used in a long-wavelength approximation. These generalized variables remain invariant under time slicing changes on long wavelengths. The equations they obey are relatively simple and can be formulated for a number of time slicing choices. Initial conditions are set after horizon crossing and the subsequent evolution is fully non-linear. We briefly discuss how these methods can be implemented numerically in the study of non-Gaussian signatures from specific inflationary models.Comment: 10 pages, replaced to match JCAP versio

Post-Inflationary Reheating

We study a model for reheating that has been much investigated for parametric resonance, having a quartic interaction of the scalar inflaton with another scalar field. Attention is particularly on the quantum excitations of the inflaton field and the metric perturbation with a smooth transition from quantum to classical stochastic states, followed through from a specific inflation model to a state including a relativistic fluid. The scalar fields enter non-perturbatively but the metric enters perturbatively, and the validity of this latter is assessed. In this model our work seems to point the large scale curvature parameter changing.Comment: 25 pages, 6 figures. Coding error(misprint) corrected:figures and some conclusions change

Stochastic Development Regression on Non-Linear Manifolds

We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is based on stochastic development of Euclidean diffusion processes to the manifold. Defining the data distribution as the transition distribution of the mapped stochastic process, parameters of the model, the non-linear analogue of design matrix and intercept, are found via maximum likelihood. The model is intrinsically related to the geometry encoded in the connection of the manifold. We propose an estimation procedure which applies the Laplace approximation of the likelihood function. A simulation study of the performance of the model is performed and the model is applied to a real dataset of Corpus Callosum shapes