7,432 research outputs found
Precise calculation of transition frequencies of hydrogen and deuterium based on a least-squares analysis
We combine a limited number of accurately measured transition frequencies in
hydrogen and deuterium, recent quantum electrodynamics (QED) calculations, and,
as an essential additional ingredient, a generalized least-squares analysis, to
obtain precise and optimal predictions for hydrogen and deuterium transition
frequencies. Some of the predicted transition frequencies have relative
uncertainties more than an order of magnitude smaller than that of the g-factor
of the electron, which was previously the most accurate prediction of QED.Comment: 4 pages, RevTe
First passage time distribution for a random walker on a random forcing energy landscape
We present an analytical approximation scheme for the first passage time
distribution on a finite interval of a random walker on a random forcing energy
landscape. The approximation scheme captures the behavior of the distribution
over all timescales in the problem. The results are carefully checked against
numerical simulations.Comment: 16 page
Nonlocal restoration of two-mode squeezing in the presence of strong optical loss
We present the experimental realization of a theoretical effect discovered by
Olivares and Paris, in which a pair of entangled optical beams undergoing
independent losses can see nonlocal correlations restored by the use of a
nonlocal resource correlating the losses. Twin optical beams created in an
entangled Einstein-Podolsky-Rosen (EPR) state by an optical parametric
oscillator above threshold were subjected to 50% loss from beamsplitters in
their paths. The resulting severe degradation of the signature quantum
correlations observed between the two beams was then suppressed when another,
independent EPR state impinged upon the other input ports of the beamsplitters,
effectively entangling the losses inflicted to the initial EPR state. The
additional EPR beam pair was classically coherent with the primary one but had
no quantum correlations with it. This result may find applications as a quantum
tap for entanglement.Comment: 14 pages, 6 figures, submitted for publicatio
Kerr-Schild type initial data for black holes with angular momenta
Generalizing previous work we propose how to superpose spinning black holes
in a Kerr-Schild initial slice. This superposition satisfies several physically
meaningful limits, including the close and the far ones. Further we consider
the close limit of two black holes with opposite angular momenta and explicitly
solve the constraint equations in this case. Evolving the resulting initial
data with a linear code, we compute the radiated energy as a function of the
masses and the angular momenta of the black holes.Comment: 13 pages, 3 figures. Revised version. To appear in Classical and
Quantum Gravit
Detection of Gravitational Lensing in the Cosmic Microwave Background
Gravitational lensing of the cosmic microwave background (CMB), a
long-standing prediction of the standard cosmolgical model, is ultimately
expected to be an important source of cosmological information, but first
detection has not been achieved to date. We report a 3.4 sigma detection, by
applying quadratic estimator techniques to all sky maps from the Wilkinson
Microwave Anisotropy Probe (WMAP) satellite, and correlating the result with
radio galaxy counts from the NRAO VLA Sky Survey (NVSS). We present our
methodology including a detailed discussion of potential contaminants. Our
error estimates include systematic uncertainties from density gradients in
NVSS, beam effects in WMAP, Galactic microwave foregrounds, resolved and
unresolved CMB point sources, and the thermal Sunyaev-Zeldovich effect.Comment: 27 pages, 20 figure
Accelerating relativistic reference frames in Minkowski space-time
We study accelerating relativistic reference frames in Minkowski space-time
under the harmonic gauge. It is well-known that the harmonic gauge imposes
constraints on the components of the metric tensor and also on the functional
form of admissible coordinate transformations. These two sets of constraints
are equivalent and represent the dual nature of the harmonic gauge. We explore
this duality and show that the harmonic gauge allows presenting an accelerated
metric in an elegant form that depends only on two harmonic potentials. It also
allows reconstruction of the spatial structure of the post-Galilean coordinate
transformation functions relating inertial and accelerating frames. The
remaining temporal dependence of these functions together with corresponding
equations of motion are determined from dynamical conditions, obtained by
constructing the relativistic proper reference frame of an accelerated test
particle. In this frame, the effect of external forces acting on the observer
is balanced by the fictitious frame-reaction force that is needed to keep the
test particle at rest with respect to the frame, conserving its relativistic
linear momentum. We find that this approach is sufficient to determine all the
terms of the coordinate transformation. The same method is then used to develop
the inverse transformations. The resulting post-Galilean coordinate
transformations extend the Poincar\'e group on the case of accelerating
observers. We present and discuss the resulting coordinate transformations,
relativistic equations of motion, and the structure of the metric tensors
corresponding to the relativistic reference frames involved.Comment: revtex4, 21 page
Ultrafast Optical Modulation of Second- and Third-Harmonic Generation from Cut-Disk-Based Metasurfaces
We design and fabricate a metasurface composed of gold cut-disk
resonators that exhibits a strong coherent nonlinear response. We experimentally
demonstrate all-optical modulation of both second- and third-harmonic signals on a
subpicosecond time scale. Pump−probe experiments and numerical models show
that the observed effects are due to the ultrafast response of the electronic
excitations in the metal under external illumination. These effects pave the way for
the development of novel active nonlinear metasurfaces with controllable and
switchable coherent nonlinear response
Windings of the 2D free Rouse chain
We study long time dynamical properties of a chain of harmonically bound
Brownian particles. This chain is allowed to wander everywhere in the plane. We
show that the scaling variables for the occupation times T_j, areas A_j and
winding angles \theta_j (j=1,...,n labels the particles) take the same general
form as in the usual Brownian motion. We also compute the asymptotic joint laws
P({T_j}), P({A_j}), P({\theta_j}) and discuss the correlations occuring in
those distributions.Comment: Latex, 17 pages, submitted to J. Phys.
Reconstruction of three-dimensional porous media using generative adversarial neural networks
To evaluate the variability of multi-phase flow properties of porous media at
the pore scale, it is necessary to acquire a number of representative samples
of the void-solid structure. While modern x-ray computer tomography has made it
possible to extract three-dimensional images of the pore space, assessment of
the variability in the inherent material properties is often experimentally not
feasible. We present a novel method to reconstruct the solid-void structure of
porous media by applying a generative neural network that allows an implicit
description of the probability distribution represented by three-dimensional
image datasets. We show, by using an adversarial learning approach for neural
networks, that this method of unsupervised learning is able to generate
representative samples of porous media that honor their statistics. We
successfully compare measures of pore morphology, such as the Euler
characteristic, two-point statistics and directional single-phase permeability
of synthetic realizations with the calculated properties of a bead pack, Berea
sandstone, and Ketton limestone. Results show that GANs can be used to
reconstruct high-resolution three-dimensional images of porous media at
different scales that are representative of the morphology of the images used
to train the neural network. The fully convolutional nature of the trained
neural network allows the generation of large samples while maintaining
computational efficiency. Compared to classical stochastic methods of image
reconstruction, the implicit representation of the learned data distribution
can be stored and reused to generate multiple realizations of the pore
structure very rapidly.Comment: 21 pages, 20 figure
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