Speaker
Description
New runs of the Large Hadron Collider and next generation of colliding
experiments with increased luminosity will require an unprecedented
amount of simulated events to be produced. This would bring an extreme
challenge to the computing resources. Thus new approaches to events
generation and simulation of detector responses are needed. Cherenkov
detectors, being relatively slow to simulate, are well suited for
applying recent approaches to fast simulations using neural networks. We
propose a way to simulate cherenkov detector response using a generative
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 particle. 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 implication of these results.
