The new Fast Calorimeter Simulation in ATLAS

ATLAS relies on very large samples of simulated events for delivering high-quality and competitive physics results, but producing these samples is very CPU intensive when using the full GEANT4 detector simulation. Fast simulation tools are a useful way of reducing CPU requirements when detailed detector simulations are not needed. During Run 1 and 2 of the LHC, a fast calorimeter simulation (FastCaloSim) was successfully used in ATLAS. FastCaloSim provides a simulation of the particle energy response at the calorimeter read-out cell level, taking into account the detailed particle shower shapes and the correlations between the energy depositions in the various calorimeter layers. It is interfaced with the standard ATLAS digitization and reconstruction software, and it can be tuned to data more easily than GEANT4. An improved version of FastCaloSim that incorporates the experience gained with the Run 1 version is currently under development. The new FastCaloSim makes use of machine learning techniques, such as principal component analysis and neural networks, to optimise the amount of information stored in the ATLAS simulation infrastructure. This allows for further performance improvement by reducing the I/O time and the memory usage during the simulation jobs. A prototype is being tested and validated, and it has shown significant improvements in the modelling of cluster-level variables in electromagnetic and hadronic showers.