Self-supervised Anomaly Detection for New Physics

Understand

We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions.

• We train a transformer neural network to encode simulated QCD "event space" dijets into a low-dimensional "latent space" representation.
• We optimize the network using the self-supervised contrastive loss, which encourages the preservation of known physical symmetries of the dijets.
• We then train a binary classifier to discriminate a BSM resonant dijet signal from a QCD dijet background both in the event space and the latent space representations.