Speaker
Dalila Salamani
(CERN)
Description
The extensive physics program of HEP experiments relies on simulated Monte Carlo events. This simulation provides a highly detailed detector response modeling. However, this simulation dominated by the calorimeter showers becomes very slow in the context of high luminosity LHC. Collecting order of magnitude more data remains necessary to lower the statistical uncertainties. Several research directions investigated the use of Machine Learning (ML) based models to for fast simulation of one specific detector. Each models tries to mimic the subtle and complex detector response resulting in a very finely tuned simulation. In this study, we explore the use of a ML multi-detector geometry model for fast simulation.
| Affiliation | CERN |
|---|
Primary author
Dalila Salamani
(CERN)
Co-authors
Anna Zaborowska
(CERN)
Witold Pokorski
(CERN)
