  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. The ability to use real data in the
pre-training phase eliminates the need to generate a large amount of training
data by simulation and mitigates bias in the choice of physics processes in the
training data. Our experiments using CMS open data confirmed that high event
classification performance can be achieved by introducing a pre-trained model.
This pre-training strategy provides a potential approach to save computational
resources for future collider experiments and introduces a foundation model for
event classification.
