Plasma formation from ultracold Rydberg gases

Recent experiments have demonstrated the spontaneous evolution of a gas of ultracold Rydberg atoms into an expanding ultracold plasma, as well as the reverse process of plasma recombination into highly excited atomic states. Treating the evolution of the plasma on the basis of kinetic equations, while ionization/excitation and recombination are incorporated using rate equations, we have investigated theoretically the Rydberg-to-plasma transition. Including the influence of spatial correlations on the plasma dynamics in an approximate way we find that ionic correlations change the results only quantitatively but not qualitatively

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Investigating the dynamics of fast capillary discharges leads to soft X-ray laser realization at 46.9 nm

The dynamic evolution of plasma columns, generated by a fast capillary discharge setup, was measured by an "off-axis" imaging apparatus. Appropriate initial conditions were found where stable compression of the plasma column could be obtained. The appearance of a sequence of shock and refraction waves during the plasma column compression is clearly seen. Relative line emission from Ne-like and F-like Ar ions at [MATH] spectral range was used for electron temperature and density measurements, during the column compression. Appropriate conditions for soft x-ray laser operation were found at the time the first shock wave reaches the axis. Indeed, strong soft x-ray amplification at 46.9nm (3s-3p, J=0-l) was measured at this stage. The small signal gain of the measured amplified line is ~0.9 cm-1and the beam divergence is less than 5mRad