Speaker
Peter Rangi Sorrenson
(Universität Heidelberg)
Description
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 representations with alternative representations using linear classifier tests and demonstrate its performance on an anomaly detection task.
Primary authors
Dr
Barry Dillon
(University of Heidelberg)
Gregor Kasieczka
(Hamburg University (DE))
Mr
Lorenz Vogel
(Heidelberg University)
Peter Rangi Sorrenson
(Universität Heidelberg)
Tilman Plehn
