A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.
Cited by 4
Biscarat et al., Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
EPJ Web Conf. 251, 03003 (2021) [Crossref]
Maevskiy et al., Simulating the time projection chamber responses at the MPD detector using generative adversarial networks
Eur. Phys. J. C 81, 599 (2021) [Crossref]
Biscarat et al., Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models
EPJ Web Conf. 251, 03049 (2021) [Crossref]
Aylett-Bullock et al., Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
J. High Energ. Phys. 2021, 66 (2021) [Crossref]
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Universität Hamburg / University of Hamburg [UH]
- 3 Lawrence Berkeley National Laboratory [LBNL]