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

TL;DR

Lamarr is a new, ultra-fast simulation framework for LHCb that uses machine learning models to significantly reduce computational time while maintaining accuracy, addressing the increasing demand for simulated data in high-energy physics experiments.

Contribution

This paper introduces Lamarr, a novel Gaudi-based fast simulation framework utilizing deep generative models and decision trees, validated to be accurate and two orders of magnitude faster.

Findings

Achieves two-order-of-magnitude speed-up compared to detailed simulation.

Maintains good agreement with detailed simulation in key reconstructed quantities.

Successfully integrates with existing LHCb simulation framework.

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. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. 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.