  The increasing luminosities of future Large Hadron Collider runs and next
generation of collider experiments will require an unprecedented amount of
simulated events to be produced. Such large scale productions are extremely
demanding in terms of computing resources. Thus new approaches to event
generation and simulation of detector responses are needed. In LHCb, the
accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU
time. An alternative approach is described here, when one generates high-level
reconstructed observables using a generative neural network to bypass low level
details. This network is trained to reproduce the particle species likelihood
function values based on the track kinematic parameters and detector occupancy.
The fast simulation is trained using real data samples collected by LHCb during
run 2. We demonstrate that this approach provides high-fidelity results.
