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

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, Bi2Sr2CuO6+δ (Bi2201) and Bi2Sr2CaCuO8+δ (Bi2212). We find prominent peak structures in both normal and anomalous self-energies, but they cancel in the total self-energy making the structure apparently invisible, while the peaks make universally dominant contributions to superconducting gap, hence evidencing the signal that generates the high-Tc superconductivity. The relation between superfluid density and critical temperature supports involvement of universal carrier relaxation associated with dissipative strange metals, where enhanced superconductivity is promoted by entangled quantum-soup nature of the cuprates. The present achievement opens avenues for innovative machine-learning spectroscopy method to reveal fundamental properties hidden in direct experimental accesses.