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 though 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.
@article{arxiv.2108.04253,
title = {Symmetries, Safety, and Self-Supervision},
author = {Barry M. Dillon and Gregor Kasieczka and Hans Olischlager and Tilman Plehn and Peter Sorrenson and Lorenz Vogel},
journal= {arXiv preprint arXiv:2108.04253},
year = {2023}
}