Speaker
Dr
Barry Dillon
(University of 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 representation with alternative representations using linear classifier tests and find it to work quite well.
Authors
Dr
Barry Dillon
(University of Heidelberg)
Gregor Kasieczka
(Hamburg University (DE))
Mr
Hans Olischlager
(Heidelberg University)
Lorenz Vogel
(Heidelberg University)
Peter Rangi Sorrenson
(Universität Heidelberg)
Tilman Plehn
