287,872 Supermassive Black Holes Masses: Deep Learning Approaching Reverberation Mapping Accuracy

We present a population-scale catalogue of 287,872 supermassive black hole masses with high accuracy. Using a deep encoder-decoder network trained on optical spectra with reverberation-mapping (RM) based labels of 849 quasars and applied to all SDSS quasars up to z=4, our method achieves a root-mean-square error of 0.058,dex, a relative uncertainty of ≈ 14%, and coefficient of determination R2≈0.91 with respect to RM-based masses, far surpassing traditional single-line virial estimators. Notably, the high accuracy is maintained for both low (<107.5,M_odot) and high (>109,M_odot) mass quasars, where empirical relations are unreliable.