Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
This addresses the computing resource bottleneck for particle physics experiments like LHCb, enabling faster event simulation, though it is incremental as it applies an existing method to a specific domain.
The paper tackles the high computational cost of simulating Cherenkov detectors for LHC experiments by using a generative neural network to generate high-level observables, bypassing low-level details, and demonstrates high-fidelity results.
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.