Open Access
Open access
Journal of Physics: Conference Series, volume 2438, issue 1, pages 12130

Towards Reliable Neural Generative Modeling of Detectors

Publication typeJournal Article
Publication date2023-02-01
SJR0.180
CiteScore1.2
Impact factor
ISSN17426588, 17426596
Computer Science Applications
History
Education
Abstract

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

Top-30

Journals

1
1

Publishers

1
1
  • We do not take into account publications without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Share
Cite this
GOST | RIS | BibTex | MLA
Found error?