Speaker
Olmo Cerri
(California Institute of Technology (US))
Description
Mitigation of the effect of the multiple parasitic proton collisions produced during bunch crossing at the LHC is a major endeavor towards the realization of the physics program at the collider. The pileup affects many physics observable derived during the online and offline reconstruction. We propose a graph neural network machine learning model, based on the PUPPI approach, for identifying particle coming from pileup and retaining the ones from high-transverse momentum collisions. We show improvement in pileup rejection performance and energy resolution with respect to solutions currently used at the LHC.
Authors
Dr
Jean-Roch Vlimant
(California Institute of Technology (US))
Maurizio Pierini
(CERN)
Olmo Cerri
(California Institute of Technology (US))
Jesus Arjona Martinez
(California Institute of Technology (US))
Maria Spiropulu
(California Institute of Technology)
