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The application of Bayesian Neural Networks(BNN) to discriminate neutrinoevents from backgrounds in reactor neutrino experiments has been described inRef.\cite{key-1}. In the paper, BNN are also used to identify neutrino eventsin reactor neutrino experiments, but the numbers of photoelectrons received byPMTs are used as inputs to BNN in the paper, not the reconstructed energy andposition of events. The samples of neutrino events and three major backgroundsfrom the Monte-Carlo simulation of a toy detector are generated in the signalregion. Compared to the BNN method in Ref.\cite{key-1}, more $^{8}$He/$^{9}$Libackground and uncorrelated background in the signal region can be rejected bythe BNN method in the paper, but more fast neutron background events in thesignal region are unidentified using the BNN method in the paper. Theuncorrelated background to signal ratio and the $^{8}$He/$^{9}$Li background tosignal ratio are significantly improved using the BNN method in the paper incomparison with the BNN method in Ref.\cite{key-1}. But the fast neutronbackground to signal ratio in the signal region is a bit larger than the one inRef.\cite{key-1}.

Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments