Machine Learning for the LHCb Simulation

TL;DR

This paper explores how Machine Learning can significantly accelerate detector simulation processes for the LHCb experiment, addressing future resource challenges due to increased data demands.

Contribution

It introduces Machine Learning techniques as a novel approach to improve the efficiency of LHCb detector simulation workflows.

Findings

Machine Learning can reduce simulation time.

Potential to meet increased resource demands.

Improved simulation efficiency for future runs.

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.