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The goal of event classification in collider physics is to distinguish signalevents of interest from background events to the extent possible to search fornew phenomena in nature. We propose a decay-aware neural network based on amulti-task learning technique to effectively address this event classification.The proposed model is designed to learn the domain knowledge of particle decaysas an auxiliary task, which is a novel approach to improving learningefficiency in the event classification. Our experiments using simulation dataconfirmed that an inductive bias was successfully introduced by adding theauxiliary task, and significant improvements in the event classification wereachieved compared with boosted decision tree and simple multi-layer perceptronmodels.

Decay-aware neural network for event classification in collider physics