Fast calorimeter simulation in LHCb

Fedor Ratnikov, Egor Zakharov, LHCb collaboration
2019 Proceedings of The 39th International Conference on High Energy Physics — PoS(ICHEP2018)   unpublished
In HEP experiments, CPU resources required by MC simulations constantly grow and become a very large fraction of the required total computing power (greater than 75%). At the same time, the pace of performance improvements from technology is slowing down. The only solution is a more efficient use of resources. LHC experiments seek options for simulating higher statistics events in a faster way. A key to the success of this strategy is the possibility of enabling fast simulation options in a
more » ... on options in a common framework with minimal action by the final user. In this paper, we describe the solution to selectively exclude particles from being simulated by the Geant4 toolkit and to insert the corresponding hits generated in a faster way. The approach, integrated within the Geant4 toolkit, has been applied to the LHCb calorimeter, but it could also be used for other subdetectors. The hits generation can be carried out by any external tool, such as a static library of showers or more complex machine-learning techniques. Generative models, which are nowadays widely used for computer vision and image processing, are being studied as a candidate to accelerate the generation of showers in the calorimeter. We present how both approaches can be applied to the LHCb calorimeter simulation, their advantages as well as their drawbacks.
doi:10.22323/1.340.0162 fatcat:6t4zdaquwnh57azpizyikrd7bm