Identifying Chern numbers of superconductors from local measurements

TL;DR

This paper presents a machine-learning method to identify the topological phases of 2D chiral superconductors using local density of states measurements, addressing a longstanding experimental challenge.

Contribution

The authors develop a robust machine-learning protocol that classifies topological states from local measurements, enabling easier experimental identification of topological superconductors.

Findings

Successfully classifies topological phases from LDOS data

Overcomes limitations of transport-based identification methods

Applicable to various 2D topological superconductors

Abstract

Fascination in topological materials originates from their remarkable response properties and exotic quasiparticles which can be utilized in quantum technologies. In particular, large-scale efforts are currently focused on realizing topological superconductors and their Majorana excitations. However, determining the topological nature of superconductors with current experimental probes is an outstanding challenge. This shortcoming has become increasingly pressing due to rapidly developing designer platforms which are theorized to display very rich topology and are better accessed by local probes rather than transport experiments. We introduce a robust machine-learning protocol for classifying the topological states of two-dimensional (2D) chiral superconductors and insulators from local density of states (LDOS) data. Since the LDOS can be measured with standard experimental techniques, our protocol contributes to overcoming the almost three decades standing problem of identifying the topological phase of 2D superconductors with broken time-reversal symmetry.