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