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In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy–momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In this work, we employ a shallow variational auto-encoder neural network to demonstrate the prospect of using ML for both denoising of as well as feature extraction from ARPES dispersion maps.

The formation of Cooper pairs, a bound state of two electrons of opposite spin and momenta by exchange of a phonon, is a defining feature of conventional superconductivity. In the cuprate high temperature superconductors, even though the superconducting state also consists of Cooper pairs, the pairing mechanism remains intensely debated. Here, we investigate superconducting pairing in the Bi2Sr2CaCu2O8+δ (Bi2212) cuprate by employing spectral functions obtained from angle-resolved photoemission as input to the Bethe-Salpeter equation. Assuming Cooper pairing is driven by spin fluctuations, we construct the spin-fluctuation-mediated pairing interaction and use it to compute the eigenfunctions and eigenvalues of the Bethe-Salpeter equation for multiple Bi2212 samples. The leading d-wave eigenvalue increases as the temperature is decreased toward Tc, reaching a value of approximately 1 at the Tc corresponding to each doping value. This suggests that spin fluctuations can approximately account for Tc and mediate pairing in the cuprate superconductors.

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