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High Energy Physics - Experiment
Title: Training towards significance with the decorrelated event classifier transformer neural network
(Submitted on 31 Dec 2023 (v1), last revised 25 Apr 2024 (this version, v2))
Abstract: 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.
Submission history
From: Jaebak Kim [view email][v1] Sun, 31 Dec 2023 08:57:29 GMT (951kb,D)
[v2] Thu, 25 Apr 2024 06:28:42 GMT (950kb,D)
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