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

Authors :
Anderlini Lucio
Barbetti Matteo
Capelli Simone
Corti Gloria
Davis Adam
Derkach Denis
Kazeev Nikita
Maevskiy Artem
Martinelli Maurizio
Mokonenko Sergei
Siddi Benedetto G.
Xu Zehua
Source :
EPJ Web of Conferences, Vol 295, p 03040 (2024)
Publication Year :
2024
Publisher :
EDP Sciences, 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.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
2100014X
Volume :
295
Database :
Directory of Open Access Journals
Journal :
EPJ Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.34de3d74fad64d83abc9a05314543c37
Document Type :
article
Full Text :
https://doi.org/10.1051/epjconf/202429503040