Hidden self-energies as origin of cuprate superconductivity revealed by machine learning

Understand

Experimental data are the source of understanding matter.

• However, measurable quantities are limited and theoretically important quantities are sometimes hidden.
• Nonetheless, recent progress of machine-learning techniques opens possibilities of exposing them only from available experimental data.
• In this paper, after establishing the reliability of the method in various careful benchmark tests, the Boltzmann-machine method is applied to the angle-resolved photoemission spectroscopy spectra of cuprate high temperature superconductors, Bi$_2$Sr$_2$CuO$_{6+\delta}$ (Bi2201) and Bi$_2$Sr$_2$CaCuO$_{8+\delta}$ (Bi2212).