Robust Upward Dispersion of the Neutron Spin Resonance in the Heavy Fermion Superconductor Ce1−x_{1-x}Ybx_{x}CoIn5_5

The neutron spin resonance is a collective magnetic excitation that appears in copper oxide, iron pnictide, and heavy fermion unconventional superconductors. Although the resonance is commonly associated with a spin-exciton due to the $d$($s^{\pm}$)-wave symmetry of the superconducting order parameter, it has also been proposed to be a magnon-like excitation appearing in the superconducting state. Here we use inelastic neutron scattering to demonstrate that the resonance in the heavy fermion superconductor Ce$_{1-x}$Yb$_{x}$CoIn$_5$ with $x=0,0.05,0.3$ has a ring-like upward dispersion that is robust against Yb-doping. By comparing our experimental data with random phase approximation calculation using the electronic structure and the momentum dependence of the $d_{x^2-y^2}$-wave superconducting gap determined from scanning tunneling microscopy for CeCoIn$_5$, we conclude the robust upward dispersing resonance mode in Ce$_{1-x}$Yb$_{x}$CoIn$_5$ is inconsistent with the downward dispersion predicted within the spin-exciton scenario.Comment: Supplementary Information available upon reques

Observation of two-dimensional Fermi surface and Dirac dispersion in YbMnSb2_2

We present the crystal structure, electronic structure, and transport properties of the material YbMnSb$_2$, a candidate system for the investigation of Dirac physics in the presence of magnetic order. Our measurements reveal that this system is a low-carrier-density semimetal with a 2D Fermi surface arising from a Dirac dispersion, consistent with the predictions of density functional theory calculations of the antiferromagnetic system. The low temperature resistivity is very large, suggesting scattering in this system is highly efficient at dissipating momentum despite its Dirac-like nature.Comment: 8 pages, 6 figure

Exploring Heavy Fermion Materials near a Quantum Critical Point

Results for two categories of heavy fermion systems : (1) a new type of non-centrosymmetric heavy fermion system R₂TM₁₂Pn₇ (R = rare earth, TM = transition metal, Pn = P, As), and (2) Yb substitution in CeCoIn₅, which is a heavy fermion superconductor, Ce₁-xYbxCoIn₅, are described. The phenomena seen in the system R₂TM₁₂Pn₇ are discussed in terms of strong hybridization between the localized f- electron states and the conduction electron states, such as a crossover between Fermi liquid and non-Fermi liquid behavior in Yb₂Ni₁₂P₇, or the presence of a large value of the Sommerfeld coefficient, [gamma] ̃ 650 mJ/mol Sm K² in Sm₂Ni₁₂P₇. The extraordinary electronic phenomena found in the system Ce₁-xYbxCoIn₅ include Yb valence fluctuations, a change in the Fermi surface topology, and suppression of the quantum critical field occurring at a nominal concentration of x ̃ 0.2. However, the suppression of the superconducting critical temperature with nominal Yb concentration x for bulk single crystals is much weaker than that observed in thin film samples. The actual Yb composition of bulk single crystals is found to be about 1 /3 of the nominal concentration, resolving the discrepancy between the variation of the physical properties of Ce₁- xYbxCoIn₅ single crystals and thin films with Yb concentration

Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning

Effective exploration is one of the critical factors affecting performance in deep reinforcement learning. Agents acquire data to learn the optimal policy through exploration, and if it is not guaranteed, the data quality deteriorates, which leads to performance degradation. This study investigates the effect of initial entropy, which significantly influences exploration, especially in the early learning stage. The results of this study on tasks with discrete action space show that (1) low initial entropy increases the probability of learning failure, (2) the distributions of initial entropy for various tasks are biased towards low values that inhibit exploration, and (3) the initial entropy for discrete action space varies with both the initial weight and task, making it hard to control. We then devise a simple yet powerful learning strategy to deal with these limitations, namely, entropy-aware model initialization. The proposed algorithm aims to provide a model with high initial entropy to a deep reinforcement learning algorithm for effective exploration. Our experiments showed that the devised learning strategy significantly reduces learning failures and enhances performance, stability, and learning speed