Quantum-disordered slave-boson theory of underdoped cuprates

We study the stability of the spin gap phase in the U(1) slave-boson theory of the t-J model in connection to the underdoped cuprates. We approach the spin gap phase from the superconducting state and consider the quantum phase transition of the slave-bosons at zero temperature by introducing vortices in the boson superfluid. At finite temperatures, the properties of the bosons are different from those of the strange metal phase and lead to modified gauge field fluctuations. As a result, the spin gap phase can be stabilized in the quantum critical and quantum disordered regime of the boson system. We also show that the regime of quantum disordered bosons with the paired fermions can be regarded as the strong coupling version of the recently proposed nodal liquid theory.Comment: 5 pages, Replaced by the published versio

Dominant mobility modulation by the electric field effect at the LaAlO_3 / SrTiO_3 interface

Caviglia et al. [Nature (London) 456, 624 (2008)] have found that the superconducting LaAlO_3 / SrTiO_3 interface can be gate modulated. A central issue is to determine the principal effect of the applied electric field. Using magnetotransport studies of a gated structure, we find that the mobility variation is almost five times as large as the sheet carrier density. Furthermore, superconductivity can be suppressed at both positive and negative gate bias. These results indicate that the relative disorder strength strongly increases across the superconductor-insulator transition.Comment: 4 pages, 4 figure

20 K superconductivity in heavily electron doped surface layer of FeSe bulk crystal

A superconducting transition temperature Tc as high as 100 K was recently discovered in 1 monolayer (1ML) FeSe grown on SrTiO3 (STO). The discovery immediately ignited efforts to identify the mechanism for the dramatically enhanced Tc from its bulk value of 7 K. Currently, there are two main views on the origin of the enhanced Tc; in the first view, the enhancement comes from an interfacial effect while in the other it is from excess electrons with strong correlation strength. The issue is controversial and there are evidences that support each view. Finding the origin of the Tc enhancement could be the key to achieving even higher Tc and to identifying the microscopic mechanism for the superconductivity in iron-based materials. Here, we report the observation of 20 K superconductivity in the electron doped surface layer of FeSe. The electronic state of the surface layer possesses all the key spectroscopic aspects of the 1ML FeSe on STO. Without any interface effect, the surface layer state is found to have a moderate Tc of 20 K with a smaller gap opening of 4 meV. Our results clearly show that excess electrons with strong correlation strength alone cannot induce the maximum Tc, which in turn strongly suggests need for an interfacial effect to reach the enhanced Tc found in 1ML FeSe/STO.Comment: 5 pages, 4 figure

Computationally Efficient Nonparametric Importance Sampling

The variance reduction established by importance sampling strongly depends on the choice of the importance sampling distribution. A good choice is often hard to achieve especially for high-dimensional integration problems. Nonparametric estimation of the optimal importance sampling distribution (known as nonparametric importance sampling) is a reasonable alternative to parametric approaches.In this article nonparametric variants of both the self-normalized and the unnormalized importance sampling estimator are proposed and investigated. A common critique on nonparametric importance sampling is the increased computational burden compared to parametric methods. We solve this problem to a large degree by utilizing the linear blend frequency polygon estimator instead of a kernel estimator. Mean square error convergence properties are investigated leading to recommendations for the efficient application of nonparametric importance sampling. Particularly, we show that nonparametric importance sampling asymptotically attains optimal importance sampling variance. The efficiency of nonparametric importance sampling algorithms heavily relies on the computational efficiency of the employed nonparametric estimator. The linear blend frequency polygon outperforms kernel estimators in terms of certain criteria such as efficient sampling and evaluation. Furthermore, it is compatible with the inversion method for sample generation. This allows to combine our algorithms with other variance reduction techniques such as stratified sampling. Empirical evidence for the usefulness of the suggested algorithms is obtained by means of three benchmark integration problems. As an application we estimate the distribution of the queue length of a spam filter queueing system based on real data.Comment: 29 pages, 7 figure