Year: 2021
Type: preprint
Abstract: Most of the computing resources pledged to the LHCb experiment at CERN are necessary to the production of simulated samples used to predict resolution functions on the reconstructed quantities and the reconstruction and selection efficiency. Projecting the Simulation requests to the years following the upcoming LHCb Upgrade, the relative computing resources would exceed the pledges by more than a factor of 2. In this contribution, I discuss how Machine Learning can help to speed up the Detector Simulation for the upcoming Runs of the LHCb experiment.
Source: arXiv (Cornell University)
Author: Anderlini, Lucio
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3205617780