Constraining the Properties of Dark Energy

The presence of dark energy in the Universe is inferred directly from the accelerated expansion of the Universe, and indirectly, from measurements of cosmic microwave background (CMB) anisotropy. Dark energy contributes about 2/3 of the critical density, is very smoothly distributed, and has large negative pressure. Its nature is very much unknown. Most of its discernible consequences follow from its effect on evolution of the expansion rate of the Universe, which in turn affects the growth of density perturbations and the age of the Universe, and can be probed by the classical kinematic cosmological tests. Absent a compelling theoretical model (or even a class of models), we describe dark energy by an effective equation of state w=p_X/rho_X which is allowed to vary with time. We describe and compare different approaches for determining w(t), including magnitude-redshift (Hubble) diagram, number counts of galaxies and clusters, and CMB anisotropy, focusing particular attention on the use of a sample of several thousand type Ia supernova with redshifts z < 1.7, as might be gathered by the proposed SNAP satellite. Among other things, we derive optimal strategies for constraining cosmological parameters using type Ia supernovae. While in the near term CMB anisotropy will provide the first measurements of w, supernovae and number counts appear to have the most potential to probe dark energy.Comment: 6 pages, 3 figures; proceedings of 20th Texas Symposium on Relavistic Astrophysic

Constraints on the Inner Cluster Mass Profile and the Power Spectrum Normalization from Strong Lensing Statistics

Strong gravitational lensing is a useful probe of both the intrinsic properties of the lenses and the cosmological parameters of the universe. The large number of model parameters and small sample of observed lens systems, however, have made it difficult to obtain useful constraints on more than a few parameters from lensing statistics. Here we examine how the recent WMAP measurements help improve the constraining power of statistics from the radio lensing survey JVAS/CLASS. We find that the absence of theta>3'' lenses in CLASS places an upper bound of beta<1.25 (1.60) at 68% (95%) CL on the inner density profile, rho \propto r^{-beta}, of cluster-sized halos. Furthermore, the favored power spectrum normalization is sigma_8 >= 0.7 (95% CL). We discuss two possibilities for stronger future constraints: a positive detection of at least one large-separation system, and next-generation radio surveys such as LOFAR.Comment: Scatter in concentration included; virial mass used consistently; new Fig 3. Final version published in ApJ

Offshore DC Grids as an Interconnection of Radial Systems : Protection and Control aspects

Peer reviewedPostprin

Topology assessment for 3 + 3 terminal offshore DC grid considering DC fault management

Peer reviewedPostprin

Parameterization of Dark-Energy Properties: a Principal-Component Approach

Considerable work has been devoted to the question of how to best parameterize the properties of dark energy, in particular its equation of state w. We argue that, in the absence of a compelling model for dark energy, the parameterizations of functions about which we have no prior knowledge, such as w(z), should be determined by the data rather than by our ingrained beliefs or familiar series expansions. We find the complete basis of orthonormal eigenfunctions in which the principal components (weights of w(z)) that are determined most accurately are separated from those determined most poorly. Furthermore, we show that keeping a few of the best-measured modes can be an effective way of obtaining information about w(z).Comment: Unfeasibility of a truly model-independent reconstruction of w at z>1 illustrated. f(z) left out, and w(z) discussed in more detail. Matches the PRL versio

Supervisory observer for parameter and state estimation of nonlinear systems using the DIRECT algorithm

A supervisory observer is a multiple-model architecture, which estimates the parameters and the states of nonlinear systems. It consists of a bank of state observers, where each observer is designed for some nominal parameter values sampled in a known parameter set. A selection criterion is used to select a single observer at each time instant, which provides its state estimate and parameter value. The sampling of the parameter set plays a crucial role in this approach. Existing works require a sufficiently large number of parameter samples, but no explicit lower bound on this number is provided. The aim of this work is to overcome this limitation by sampling the parameter set automatically using an iterative global optimisation method, called DIviding RECTangles (DIRECT). Using this sampling policy, we start with 1 + 2np parameter samples where np is the dimension of the parameter set. Then, the algorithm iteratively adds samples to improve its estimation accuracy. Convergence guarantees are provided under the same assumptions as in previous works, which include a persistency of excitation condition. The efficacy of the supervisory observer with the DIRECT sampling policy is illustrated on a model of neural populations

Qubit-Initialisation and Readout with Finite Coherent Amplitudes in Cavity QED

We consider a unitary transfer of an arbitrary state of a two-level atomic qubit in a cavity to the finite amplitude coherent state cavity field. Such transfer can be used to either provide an effective readout measurement on the atom by a subsequent measurement on the light field or as a method for initializing a fixed atomic state - a so-called "attractor state", studied previously for the case of an infinitely strong cavity field. We show that with a suitable adjustment of the coherent amplitude and evolution time the qubit transfers all its information to the field, attaining a selected state of high purity irrespectively of the initial state.Comment: 6 pages, 4 figure