Speaker
Jonas Spinner
(Durham University)
Description
We study for the first time the benefit of Lorentz-equivariant transformers for large-size jet tagging and flavor tagging. To control their computing demands, we optimize their implementations for inference cost metrics. In our scaling studies, we find that Lorentz-equivariant networks outperform standard transformers provided geometric features are relevant. This holds true in an idealized world as well as for limited resources. The limited gain from Lorentz-equivariance provides interesting input to the development of foundation models for LHC data.
Authors
Huilin Qu
(CERN)
Jonas Spinner
(Durham University)
Luigi Favaro
(Universite Catholique de Louvain (UCL) (BE))
Tilman Plehn