Training towards significance with the decorrelated

event classifier transformer neural network

김재박 박사 


2024년 5월 13일(월), 17시

첨단물리세미나실(아산이학관 440A호)

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. 

(, accepted to PRD.