Speaker
Description
To achieve higher physics precision, the LHCb experiment is operating at an increased instantaneous luminosity in Run 3, leading to an unprecedented challenge in total data volume. A single proton-proton collision generates hundreds of tracks, yet the target signals involve only a few; this imbalance severely inflates the event data size. To efficiently reduce the event size while retaining the physics information required for targeted analyses, a suite of inclusive isolation tools has been developed, featuring both the classical methods and a novel Inclusive Multivariate Isolation (IMI) algorithm. The IMI tool is designed to robustly distinguish the signal tracks from high pile-up background tracks, adapting the strengths of traditional isolation techniques while handling the diverse topologies and kinematics across various decay chains. These tools are currently deployed within the Run 3 LHCb software framework for both the High-Level Trigger and the offline reconstruction chain. By achieving a 45% reduction in total data size, the IMI tool preserves full physics performance, demonstrating a high selection efficiency of over 99% for target signal particles. Crucially, its robustness and stability have been validated under real data-taking conditions. Looking forward, the IMI methodology shows great potential as a fast, lightweight approach to support more compute-intensive selection strategies in the high-multiplicity environment of the High-Luminosity LHC.
(This work is based on a paper submitted to the journal, Computer Software and Big Science (CSBS).)