Predicting the black hole mass and correlations in X-ray reverberating AGN using neural networks
P. Chainakun,
I. Fongkaew,
S. Hancock
et al.
Abstract:We develop neural network models to predict the black hole mass using 22 reverberating AGN samples in the XMM-Newton archive. The model features include the fractional excess variance (F var ) in 2-10 keV band, Fe-K lag amplitude, 2-10 keV photon counts and redshift. We find that the prediction accuracy of the neural network model is significantly higher than what is obtained from the traditional linear regression method. Our predicted mass can be confined within ±(2-5) per cent of the true value, suggesting t… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.