Cherenkov Detectors Fast Simulation Using Neural Networks
arXiv:1903.11788 · doi:10.1016/j.nima.2019.01.031
Abstract
We propose a way to simulate Cherenkov detector response using a generative adversarial 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 particles. 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 implications of these results.
In proceedings of 10th International Workshop on Ring Imaging Cherenkov Detectors
References in corpus (4)
Cited by in corpus (17)
- Anomaly Detection with Density Estimation
- Machine Learning in Nuclear Physics
- Score-based Generative Models for Calorimeter Shower Simulation
- CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
- A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
- A factorisation-aware Matrix element emulator
- Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
- A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
- Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
- DCTRGAN: Improving the Precision of Generative Models with Reweighting
- Towards a Deep Learning Model for Hadronization
- Parameter Estimation using Neural Networks in the Presence of Detector Effects
- DeepRICH: Learning Deeply Cherenkov Detectors
- Machine Learning for Imaging Cherenkov Detectors
- Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
- Generative models uncertainty estimation
- Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)