Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks
May 2, 20188 pages
Published in:
- Comput.Softw.Big Sci. 2 (2018) 1, 8
- Published: Nov 2, 2018
e-Print:
- 1805.00850 [hep-ex]
View in:
Citations per year
Abstract: (Springer)
Deep generative models parametrised by neural networks have recently started to provide accurate results in modeling natural images. In particular, generative adversarial networks provide an unsupervised solution to this problem. In this work, we apply this kind of technique to the simulation of particle detector response to hadronic jets. We show that deep neural networks can achieve high fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms.Note:
- Published on Comp. Soft. for Big Sci
- Generative adversarial networks
- Deep learning
- High-energy physics
- Simulation
- Fast simulation
- Jet images
- CERN open data
- jet: hadronic
- track data analysis: jet
- neural network
References(52)
Figures(49)