Symmetries, safety, and self-supervision

Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables through self-supervised contrastive learning. As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties. We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well.

Automated summary

This paper introduces the JetCLR method to solve the mapping problem from low-level data to optimized observables through self-supervised contrastive learning. A symmetric data representation is constructed using a permutation-invariant transformer-encoder network for top and QCD jets, and its performance is compared with alternative representations using linear classifier tests, showing good results.