Speaker
Olmo Cerri
(California Institute of Technology (US))
Description
We show how Interaction Networks could be used for jet tagging at the Large Hadron Collider.
We take as an example the problem of identifying high-pT H->bb decays exploiting both jet substructure and secondary vertices from b quarks. We consider all tracks produced in the hadronization of the two b’s and represent the jet both as a track-to-track and a track-to-vertex interaction. The representations of the two interactions are learned training two dense neural networks. The derived information is used to train a classifier of H->bb jets. Interaction networks achieve state-of-the-art discrimination performances, even when the training is prevented from learning to exploit the jet mass value as discriminating information.
Primary authors
Jean-Roch Vlimant
(California Institute of Technology (US))
Javier Mauricio Duarte
(Fermi National Accelerator Lab. (US))
Maria Spiropulu
(California Institute of Technology (US))
Maurizio Pierini
(CERN)
Thong Nguyen
(California Institute of Technology (US))
Eric Moreno
(California Institute of Technology)
Joosep Pata
(California Institute of Technology (US))
Avikar Periwal
(California Institute of Technology (US))
Olmo Cerri
(California Institute of Technology (US))
