4-8 November 2019
Adelaide Convention Centre
Australia/Adelaide timezone

Generative Adversarial Networks for LHCb Fast Simulation

4 Nov 2019, 15:00
Riverbank R6 (Adelaide Convention Centre)

Riverbank R6

Adelaide Convention Centre

Oral Track 2 – Offline Computing Track 2 – Offline Computing


Fedor Ratnikov (Yandex School of Data Analysis (RU))


LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.

Consider for promotion Yes

Primary authors

Denis Derkach (National Research University Higher School of Economics (RU)) Fedor Ratnikov (Yandex School of Data Analysis (RU))

Presentation Materials