Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

gg-wave Pairing in BiS2_2 Superconductors

Recent angle resolved photoemission spectroscopy(ARPES) experiments have suggested that BiS$_2$ based superconductors are at very low electron doping. Using random phase approximation(RPA) and functional renormalization group(FRG) methods, we find that $g$-wave pairing symmetry belonging to A$_{2g}$ irreducible representation is dominant at electron doping $x<0.25$. The pairing symmetry is determined by inter-pocket nesting and orbital characters on the Fermi surfaces and is robust in a two-orbital model including both Hund's coupling $J$, and Hubbard-like Coulomb interactions $U$ and $U'$ with relatively small $J$ ($J\leq0.2U$). With the increasing electron doping, the g-wave state competes with both the s-wave $A_{1g}$ and d-wave $B_{2g}$ states and no pairing symmetry emerges dominantly.Comment: published version, EPL(editor's choice