Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
arXiv:1905.11825 · doi:10.1088/1742-6596/1525/1/012097
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.
arXiv commentProceedings for 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. (Fixed typos and added one missing reference in the revised version.)
References in corpus (2)
Cited by in corpus (10)
- Machine Learning in Nuclear Physics
- The LHCb upgrade I
- Conditional Born machine for Monte Carlo event generation
- Uncertainties associated with GAN-generated datasets in high energy physics
- Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
- OASIS: Optimal Analysis-Specific Importance Sampling for event generation
- Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200
- The LHCb ultra-fast simulation option, Lamarr: design and validation
- Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)
- Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses