Towards Reliable Neural Generative Modeling of Detectors
This work addresses the problem of high computing resource demands for physicists in particle physics experiments, but it appears incremental as it focuses on applying existing GAN methods to a specific detector simulation.
The paper tackles the computational challenge of simulating large-scale detector events for collider experiments by applying generative adversarial networks (GANs) to model the LHCb experiment, achieving results based on Geant4 simulations without specifying concrete performance numbers.
The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.