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.

Lamarr 是一个基于 Gaudi 的框架，旨在为 LHCb 探测器的模拟提供最快的解决方案，通过参数化模块将探测器响应和重建算法进行模拟，使用深度生成模型和梯度提升决策树进行参数化，结合 LHCb Gauss 模拟框架和现有的发生器进行高效模拟，实现与详细模拟相比的较好的模拟结果并提高两个数量级的模拟速度。