  The goal of event classification in collider physics is to distinguish signal
events of interest from background events to the extent possible to search for
new phenomena in nature. We propose a decay-aware neural network based on a
multi-task learning technique to effectively address this event classification.
The proposed model is designed to learn the domain knowledge of particle decays
as an auxiliary task, which is a novel approach to improving learning
efficiency in the event classification. Our experiments using simulation data
confirmed that an inductive bias was successfully introduced by adding the
auxiliary task, and significant improvements in the event classification were
achieved compared with boosted decision tree and simple multi-layer perceptron
models.
