# Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

## TL;DR

This paper introduces a neural network-based method to rapidly simulate Cherenkov detector responses in high-energy physics, significantly reducing computational costs while maintaining high accuracy.

## Contribution

The authors develop a generative adversarial network that produces high-level detector observables from real data, bypassing detailed low-level simulation processes.

## Key findings

- Achieves high-fidelity simulation results
- Reduces CPU time for detector response simulation
- Validates approach using real LHCb data

## Abstract

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.