15,215 research outputs found
Simulations of Sisyphus cooling including multiple excited states
We extend the theory for laser cooling in a near-resonant optical lattice to
include multiple excited hyperfine states. Simulations are performed treating
the external degrees of freedom of the atom, i.e., position and momentum,
classically, while the internal atomic states are treated quantum mechanically,
allowing for arbitrary superpositions. Whereas theoretical treatments including
only a single excited hyperfine state predict that the temperature should be a
function of lattice depth only, except close to resonance, experiments have
shown that the minimum temperature achieved depends also on the detuning from
resonance of the lattice light. Our results resolve this discrepancy.Comment: 7 pages, 6 figure
Trends and challenges in VLSI technology scaling towards 100 nm
Summary form only given. Moore's Law drives VLSI technology to continuous increases in transistor densities and higher clock frequencies. This tutorial will review the trends in VLSI technology scaling in the last few years and discuss the challenges facing process and circuit engineers in the 100nm generation and beyond. The first focus area is the process technology, including transistor scaling trends and research activities for the 100nm technology node and beyond. The transistor leakage and interconnect RC delays will continue to increase. The tutorial will review new circuit design techniques for emerging process technologies, including dual Vt transistors and silicon-on-insulator. It will also cover circuit and layout techniques to reduce clock distribution skew and jitter, model and reduce transistor leakage and improve the electrical performance of flip-chip packages. Finally, the tutorial will review the test challenges for the 100nm technology node due to increased clock frequency and power consumption (both active and passive) and present several potential solution
Bayesian and Adaptive Optimal Policy under Model Uncertainty
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true economic structure is unobserved, and he optimally learns from observations of the economy. This is a classic problem of learning and control, variants of which have been studied in the past, but seldom with forward-looking variables which are a key component of modern policy-relevant models. As in most Bayesian learning problems, the optimal policy typically includes an experimentation component reflecting the endogeneity of information. We develop algorithms to solve numerically for the Bayesian optimal policy (BOP). However, computing the BOP is only feasible in relatively small models, and thus we also consider a simpler specification we term adaptive optimal policy (AOP) which allows policymakers to update their beliefs but shortcuts the experimentation motive. In our setting, the AOP is significantly easier to compute, and in many cases provides a good approximation to the BOP. We provide some simple examples to illustrate the role of learning and experimentation in an MJLQ framework.
Steep Slopes and Preferred Breaks in GRB Spectra: the Role of Photospheres and Comptonization
The role of a photospheric component and of pair breakdown is examined in the
internal shock model of gamma-ray bursts. We discuss some of the mechanisms by
which they would produce anomalously steep low energy slopes, X-ray excesses
and preferred energy breaks. Sub-relativistic comptonization should dominate in
high comoving luminosity bursts with high baryon load, while synchrotron
radiation dominates the power law component in bursts which have lower comoving
luminosity or have moderate to low baryon loads. A photosphere leading to steep
low energy spectral slopes should be prominent in the lowest baryon loadComment: ApJ'00, in press; minor revs. 10/5/99; (uses aaspp4.sty), 15 pages, 3
figure
Soft metrics and their Performance Analysis for Optimal Data Detection in the Presence of Strong Oscillator Phase Noise
In this paper, we address the classical problem of maximum-likelihood (ML)
detection of data in the presence of random phase noise. We consider a system,
where the random phase noise affecting the received signal is first compensated
by a tracker/estimator. Then the phase error and its statistics are used for
deriving the ML detector. Specifically, we derive an ML detector based on a
Gaussian assumption for the phase error probability density function (PDF).
Further without making any assumptions on the phase error PDF, we show that the
actual ML detector can be reformulated as a weighted sum of central moments of
the phase error PDF. We present a simple approximation of this new ML rule
assuming that the phase error distribution is unknown. The ML detectors derived
are also the aposteriori probabilities of the transmitted symbols, and are
referred to as soft metrics. Then, using the detector developed based on
Gaussian phase error assumption, we derive the symbol error probability (SEP)
performance and error floor analytically for arbitrary constellations. Finally
we compare SEP performance of the various detectors/metrics in this work and
those from literature for different signal constellations, phase noise
scenarios and SNR values
Superdeformed bands in neutron-rich Sulfur isotopes suggested by cranked Skyrme-Hartree-Fock calculations
On the basis of the cranked Skyrme-Hartree-Fock calculations in the
three-dimensional coordinate-mesh representation, we suggest that, in addition
to the well-known candidate 32S, the neutron-rich nucleus 36S and the drip-line
nuclei,48S and 50S, are also good candidates for finding superdeformed
rotational bands in Sulfur isotopes. Calculated density distributions for the
superdeformed states in 48S and 50S exhibit superdeformed neutron skinsComment: 18 pages including 10 ps figure
Evidence cross-validation and Bayesian inference of MAST plasma equilibria
In this paper, current profiles for plasma discharges on the Mega-Ampere
Spherical Tokamak (MAST) are directly calculated from pickup coil, flux loop
and Motional-Stark Effect (MSE) observations via methods based in the
statistical theory of Bayesian analysis. By representing toroidal plasma
current as a series of axisymmetric current beams with rectangular
cross-section and inferring the current for each one of these beams,
flux-surface geometry and q-profiles are subsequently calculated by elementary
application of Biot-Savart's law. The use of this plasma model in the context
of Bayesian analysis was pioneered by Svensson and Werner on the Joint-European
Tokamak (JET) [J. Svensson and A. Werner. Current tomography for axisymmetric
plasmas. Plasma Physics and Controlled Fusion, 50(8):085002, 2008]. In
this framework, linear forward models are used to generate diagnostic
predictions, and the probability distribution for the currents in the
collection of plasma beams was subsequently calculated directly via application
of Bayes' formula. In this work, we introduce a new diagnostic technique to
identify and remove outlier observations associated with diagnostics falling
out of calibration or suffering from an unidentified malfunction. These
modifications enable good agreement between Bayesian inference of the last
closed flux-surface (LCFS) with other corroborating data, such as such as that
from force balance considerations using EFIT++ [L. Appel et al., Proc. 33rd EPS
Conf., Rome, Italy, 2006]. In addition, this analysis also yields errors on the
plasma current profile and flux-surface geometry, as well as directly
predicting the Shafranov shift of the plasma core.This work was jointly funded by the Australian Government
through International Science Linkages Grant No.
CG130047, the Australian National University, the United
Kingdom Engineering and Physical Sciences Research
Council under Grant No. EP/G003955, and by the European
Communities under the contract of Association between EURATOM and CCFE
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