  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. We find the classifier
performances on the event and latent spaces to be comparable. We finally
perform an anomaly detection search using a weakly supervised bump hunt on the
latent space dijets, finding again a comparable performance to a search run on
the physical space dijets. This opens the door to using low-dimensional latent
representations as a computationally efficient space for resonant anomaly
detection in generic particle collision events.
