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

arXiv: https://arxiv.org/abs/1905.11825 · HTML: https://mlsanity.com/paper/1905.11825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 6 of 6)

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Conditional Born machine for Monte Carlo event generation](https://mlsanity.com/paper/2205.07674/) (2022)
- [Artificial Intelligence for Imaging Cherenkov Detectors at the EIC](https://mlsanity.com/paper/2204.08645/) (2022)
- [Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200](https://mlsanity.com/paper/2303.06311/) (2023)
- [The LHCb ultra-fast simulation option, Lamarr: design and validation](https://mlsanity.com/paper/2309.13213/) (2023)
- [Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)](https://mlsanity.com/paper/2401.05295/) (2024)

## Abstract (arXiv)

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.