Minimising Event Size, Maximising Physics: Inclusive Particle Isolation for LHCb's Run 3

TL;DR

The paper introduces the Inclusive Multivariate Isolation (IMI) algorithm for LHCb's Run 3, significantly reducing event size while maintaining high physics performance, thus enhancing data processing efficiency.

Contribution

A novel IMI algorithm that combines classical isolation methods, improving background rejection and signal efficiency, and effectively reducing data size by 45% in high-luminosity conditions.

Findings

IMI outperforms traditional isolation methods in background rejection.

IMI achieves 99% efficiency in selecting signal particles.

Event size is reduced by 45% without compromising physics performance.

Abstract

The Run 3 of the LHC brings unprecedented luminosity and a surge in data volume to the LHCb detector, necessitating a critical reduction in the size of each reconstructed event without compromising the physics reach of the heavy-flavour programme. While signal decays typically involve just a few charged particles, a single proton-proton collision produces hundreds of tracks, with charged particle information dominating the event size. To address this imbalance, a suite of inclusive isolation tools have been developed, including both classical methods and a novel Inclusive Multivariate Isolation (IMI) algorithm. The IMI unifies the key strengths of classical isolation techniques and is designed to robustly handle diverse decay topologies and kinematics, enabling efficient reconstruction of decay chains with varying final-state multiplicities. It consistently outperforms traditional methods, with superior background rejection and high signal efficiency across diverse channels and event multiplicities. By retaining only the most relevant particles in each event, the method achieves a 45 % reduction in data size while preserving full physics performance, selecting signal particles with 99% efficiency. We also validate IMI on Run 3 data, confirming its robustness under real data-taking conditions. In the long term, IMI could provide a fast, lightweight front-end to support more compute-intensive selection strategies in the high-multiplicity environment of the High-Luminosity LHC.