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We develop neural network models to predict the black hole mass using 22reverberating AGN samples in the XMM-Newton archive. The model features includethe fractional excess variance ($F_{\rm var}$) in 2-10 keV band, Fe-K lagamplitude, 2-10 keV photon counts and redshift. We find that the predictionaccuracy of the neural network model is significantly higher than what isobtained from the traditional linear regression method. Our predicted mass canbe confined within $\pm (2$-5) per cent of the true value, suggesting that theneural network technique is a promising and independent way to constrain theblack hole mass. We also apply the model to 21 non-reverberating AGN to ruleout their possibility to exhibit the lags (some have too small mass and $F_{\rmvar}$, while some have too large mass and $F_{\rm var}$ that contradict the$F_{\rm var}$-lag-mass relation in reverberating AGN). We also simulate 3200reverberating AGN samples using the multi-feature parameter space from theneural network model to investigate the global relations if the number ofreverberating AGN increases. We find that the $F_{\rm var}$-massanti-correlation is likely stronger with increasing number of newly-discoveredreverberating AGN. Contrarily, to maintain the lag-mass scaling relation, thetight anti-correlation between the lag and $F_{\rm var}$ must preserve. In anextreme case, the lag-mass correlation coefficient can significantly decreaseand, if observed, may suggest the extended corona framework where theirobserved lags are more driven by the coronal property rather than geometry.

Predicting the black hole mass and correlations in X-ray reverberating AGN using neural networks