Cherenkov Detectors Fast Simulation Using Neural Networks
This addresses a bottleneck in particle physics simulations, but it is incremental as it applies an existing method to a specific domain.
The authors tackled the slow simulation of Cherenkov detectors by using a generative adversarial neural network to bypass low-level details, resulting in a dramatic increase in simulation speed while maintaining precision consistent with the baseline.
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.