Training towards significance with the decorrelated event classifier
  transformer neural network
Jaebak Kim
Experimental particle physics uses machine learning for many of tasks, where
one application is to classify signal and background events. The classification
can be used to bin an analysis region to enhance the expected significance for
a mass resonance search. In natural language processing, one of the leading
neural network architectures is the transformer. In this work, an event
classifier transformer is proposed to bin an analysis region, in which the
network is trained with special techniques. The techniques developed here can
enhance the significance and reduce the correlation between the network's
output and the reconstructed mass. It is found that this trained network can
perform better than boosted decision trees and feed-forward networks.