9,906 research outputs found
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
A new geometric shaping method is proposed, leveraging unsupervised machine
learning to optimize the constellation design. The learned constellation
mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a
simplified fiber channel model.Comment: 3 pages, 6 figures, submitted to ECOC 201
Color-charge separation in trapped SU(3) fermionic atoms
Cold fermionic atoms with three different hyperfine states with
SU(3) symmetry confined in one-dimensional optical lattices show color-charge
separation, generalizing the conventional spin charge separation for
interacting SU(2) fermions in one dimension. Through time-dependent DMRG
simulations, we explore the features of this phenomenon for a generalized SU(3)
Hubbard Hamiltonian. In our numerical simulations of finite size systems, we
observe different velocities of the charge and color degrees of freedom when a
Gaussian wave packet or a charge (color) density response to a local
perturbation is evolved. The differences between attractive and repulsive
interactions are explored and we note that neither a small anisotropy of the
interaction, breaking the SU(3) symmetry, nor the filling impedes the basic
observation of these effects
On the two-loop electroweak amplitude of the muon decay
We present an analysis of the two-loop amplitude of the muon decay in the Standard Model (SM) using algebraic renormalization techniques. In addition, we discuss a manifestly BRST invariant IR regulator for the photon within the SM
Optimal target search on a fast folding polymer chain with volume exchange
We study the search process of a target on a rapidly folding polymer (`DNA')
by an ensemble of particles (`proteins'), whose search combines 1D diffusion
along the chain, Levy type diffusion mediated by chain looping, and volume
exchange. A rich behavior of the search process is obtained with respect to the
physical parameters, in particular, for the optimal search.Comment: 4 pages, 3 figures, REVTe
Aging dynamics in interacting many-body systems
Low-dimensional, complex systems are often characterized by logarithmically
slow dynamics. We study the generic motion of a labeled particle in an ensemble
of identical diffusing particles with hardcore interactions in a strongly
disordered, one-dimensional environment. Each particle in this single file is
trapped for a random waiting time with power law distribution
, such that the values are
independent, local quantities for all particles. From scaling arguments and
simulations, we find that for the scale-free waiting time case ,
the tracer particle dynamics is ultra-slow with a logarithmic mean square
displacement (MSD) . This extreme
slowing down compared to regular single file motion is due to the high likelihood that the labeled
particle keeps encountering strongly immobilized neighbors. For the case
we observe the MSD scaling , where we recover Harris law
.Comment: 5 pages, 4 figure
On Predicting the Solar Cycle using Mean-Field Models
We discuss the difficulties of predicting the solar cycle using mean-field
models. Here we argue that these difficulties arise owing to the significant
modulation of the solar activity cycle, and that this modulation arises owing
to either stochastic or deterministic processes. We analyse the implications
for predictability in both of these situations by considering two separate
solar dynamo models. The first model represents a stochastically-perturbed flux
transport dynamo. Here even very weak stochastic perturbations can give rise to
significant modulation in the activity cycle. This modulation leads to a loss
of predictability. In the second model, we neglect stochastic effects and
assume that generation of magnetic field in the Sun can be described by a fully
deterministic nonlinear mean-field model -- this is a best case scenario for
prediction. We designate the output from this deterministic model (with
parameters chosen to produce chaotically modulated cycles) as a target
timeseries that subsequent deterministic mean-field models are required to
predict. Long-term prediction is impossible even if a model that is correct in
all details is utilised in the prediction. Furthermore, we show that even
short-term prediction is impossible if there is a small discrepancy in the
input parameters from the fiducial model. This is the case even if the
predicting model has been tuned to reproduce the output of previous cycles.
Given the inherent uncertainties in determining the transport coefficients and
nonlinear responses for mean-field models, we argue that this makes predicting
the solar cycle using the output from such models impossible.Comment: 22 Pages, 5 Figures, Preprint accepted for publication in Ap
Entanglement of a microcanonical ensemble
We replace time-averaged entanglement by ensemble-averaged entanglement and
derive a simple expression for the latter. We show how to calculate the
ensemble average for a two-spin system and for the Jaynes-Cummings model. In
both cases the time-dependent entanglement is known as well so that one can
verify that the time average coincides with the ensemble average.Comment: 10 page
Critical dynamics of ballistic and Brownian particles in a heterogeneous environment
The dynamic properties of a classical tracer particle in a random, disordered
medium are investigated close to the localization transition. For Lorentz
models obeying Newtonian and diffusive motion at the microscale, we have
performed large-scale computer simulations, demonstrating that universality
holds at long times in the immediate vicinity of the transition. The scaling
function describing the crossover from anomalous transport to diffusive motion
is found to vary extremely slowly and spans at least 5 decades in time. To
extract the scaling function, one has to allow for the leading universal
corrections to scaling. Our findings suggest that apparent power laws with
varying exponents generically occur and dominate experimentally accessible time
windows as soon as the heterogeneities cover a decade in length scale. We
extract the divergent length scales, quantify the spatial heterogeneities in
terms of the non-Gaussian parameter, and corroborate our results by a thorough
finite-size analysis.Comment: 14 page
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