Training towards significance with the decorrelated event classifier transformer neural network

arXiv:2401.00428v2 Announce Type: replace-cross
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 …

abstract analysis application arxiv classification classifier cs.ai cs.lg event events experimental hep-ex language language processing machine machine learning natural natural language natural language processing network neural network particle particle physics physics processing search signal significance tasks training transformer type