Bulk effects on topological conduction on the surface of 3-D topological insulators

The surface states of a topological insulator in a fine-tuned magnetic field are ideal candidates for realizing a topological metal which is protected against disorder. Its signatures are (1) a conductance plateau in long wires in a finely tuned longitudinal magnetic field and (2) a conductivity which always increases with sample size, and both are independent of disorder strength. We numerically study how these experimental transport signatures are affected by bulk physics in the interior of the topological insulator sample. We show that both signatures of the topological metal are robust against bulk effects. However the bulk does substantially accelerate the metal's decay in a magnetic field and alter its response to surface disorder. When the disorder strength is tuned to resonance with the bulk band the conductivity follows the predictions of scaling theory, indicating that conduction is diffusive. At other disorder strengths the bulk reduces the effects of surface disorder and scaling theory is systematically violated, signaling that conduction is not fully diffusive. These effects will change the magnitude of the surface conductivity and the magnetoconductivity

Artificial Neural Network Approach to the Analytic Continuation Problem

Inverse problems are encountered in many domains of physics, with analytic continuation of the imaginary Green's function into the real frequency domain being a particularly important example. However, the analytic continuation problem is ill defined and currently no analytic transformation for solving it is known. We present a general framework for building an artificial neural network (ANN) that solves this task with a supervised learning approach. Application of the ANN approach to quantum Monte Carlo calculations and simulated Green's function data demonstrates its high accuracy. By comparing with the commonly used maximum entropy approach, we show that our method can reach the same level of accuracy for low-noise input data, while performing significantly better when the noise strength increases. The computational cost of the proposed neural network approach is reduced by almost three orders of magnitude compared to the maximum entropy methodComment: 6 pages, 4 figures, supplementary material available as ancillary fil