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.

Details

Title
Towards Reliable Neural Generative Modeling of Detectors
Author
Anderlini, L 1 ; Barbetti, M 2 ; Derkach, D 3 ; Kazeev, N 3 ; Maevskiy, A 3 ; Mokhnenko, S 3 

 Istituto Nazionale di Fisica Nucleare - Sezione di Firenze , via G. Sansone, 1, Sesto Fiorentino , Italy 
 Istituto Nazionale di Fisica Nucleare - Sezione di Firenze , via G. Sansone, 1, Sesto Fiorentino , Italy; Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze , via Santa Marta, 3, Firenze , Italy 
 HSE University , 20 Myasnitskaya st., Moscow 101000 , Russia 
First page
012130
Publication year
2023
Publication date
Feb 2023
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2777067721
Copyright
Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.