Speaker
Dalila Salamani
(CERN)
Description
The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. However, a detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process, making it a challenge to gather enough statistics. This is especially problematic for the upcoming High Luminosity LHC upgrade, with more complex events and a much increased trigger rate. In this talk, we discuss novel machine learning techniques that demonstrate efficient and accurate detector simulation and can be used to bridge the HL-LHC resource gap.
Primary author
Dalila Salamani
(CERN)
