The LHCb ultra-fast simulation option, Lamarr design and validation

¹ Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Firenze, Italy
² Department of Information Engineering (DINFO), University of Firenze, Italy
³ Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Milano-Bicocca, Italy
⁴ Department of Physics, University of Milano-Bicocca, Italy
⁵ European Organization for Nuclear Research (CERN), Switzerland
⁶ Department of Physics and Astronomy, University of Manchester, United Kingdom
⁷ Affiliated with an institute covered by a cooperation agreement with CERN
⁸ Department of Physics, University of Ferrara, Italy
⁹ Laboratoire de Physique de Clermont (LPC), Université Clermont Auvergne, France

^* e-mail: matteo.barbetti@cern.ch

Published online: 6 May 2024

Abstract

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. The evolution of technologies and techniques for simulation production is then mandatory to meet the upcoming needs for the analysis of most of the data collected by the LHCb experiment. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two order of magnitude speed-up of the simulation phase.