2,380 research outputs found
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
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