  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.
