ATLAS relies on very large samples of simulated events for delivering high-quality
and competitive physics results, but producing these samples takes much time and
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 the LHC Runs 1 and 2, 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 to the standard ATLAS digitization and reconstruction software, and it can
be tuned to data more easily than GEANT4.
Now an improved version of FastCaloSim is in development, incorporating the experience
with the version used during Run-1. 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 job.
A prototype is being tested and validated, and it has shown significant improvements in the
description of cluster level variables in electromagnetic and hadronic showers. ATLAS
plans to use this new FastCaloSim parameterization to simulate several billion events in the
upcoming LHC runs.
It will be combined with other fast tools used in the ATLAS production chain. In this
Fast Chain the simulation, digitisation and reconstruction of the events are handled by fast
tools. In this talk, we will describe this new FastCaloSim parametrisation and the current status
of the ATLAS Fast Chain.