Pre-training strategy using real particle collision data for event classification in collider physics

Understand

This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy.

• Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of interest from background events as much as possible to search for new phenomena in nature.
• A pre-training strategy with feasibility to efficiently train the target event classification using a small amount of training data has been proposed.
• Real particle collision data were used in the pre-training phase as a novelty, where a self-supervised learning technique to handle the unlabeled data was employed.