Superconductivity-induced Phonon Renormalization on NaFe1−x_{1-x}Cox_{x}As

We report a study of the lattice dynamics in superconducting NaFeAs (Tc = 8 K) and doped NaFe0.97Co0.03As (Tc = 20 K) using Raman light scattering. Five of the six phonon modes expected from group theory are observed. In contrast with results obtained on iso-structural and iso-electronic LiFeAs, anomalous broadening of Eg(As) and A1g(Na) modes upon cooling is observed in both samples. In addition, in the Co-doped sample, a superconductivity-induced renormalization of the frequency and linewidth of the B1g(Fe) vibration is observed. This renormalization can not be understood within a single band and simple multi-band approaches. A theoretical model that includes the effects of SDW correlations along with sign-changing s-wave pairing state and interband scattering has been developed to explain the observed behavior of the B1g(Fe) mode.Comment: 10 pages; 6 figure

Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach

Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to hyper-parameter configuration, finding an appropriate network is a tough task. Automatic hyper-parameter optimization methods have emerged to find the most suitable configuration to a given problem, but these methods are not generally adopted because of their high computational cost. Therefore, in this study we extend the MAE random sampling, a low-cost method to compare single-hidden layer architectures, to multiple-hidden-layer ones. We validate empirically our proposal and show that it is possible to predict and compare the expected performance of an hyper-parameter configuration in a low-cost way.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by Ministerio de Economı́a, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es)