  At the CMS experiment, a growing reliance on the fast Monte Carlo application
(FastSim) will accompany the high luminosity and detector granularity expected
in Phase 2. The FastSim chain is roughly 10 times faster than the application
based on the GEANT4 detector simulation and full reconstruction referred to as
FullSim. However, this advantage comes at the price of decreased accuracy in
some of the final analysis observables. In this contribution, a machine
learning-based technique to refine those observables is presented. We employ a
regression neural network trained with a sophisticated combination of multiple
loss functions to provide post-hoc corrections to samples produced by the
FastSim chain. The results show considerably improved agreement with the
FullSim output and an improvement in correlations among output observables and
external parameters. This technique is a promising replacement for existing
correction factors, providing higher accuracy and thus contributing to the
wider usage of FastSim.
