# Minimising event size, maximising physics: inclusive particle isolation for LHCb’s Run 3

## TL;DR

This paper introduces a new algorithm to reduce data size in particle physics experiments without losing important physics information.

## Contribution

The novel Inclusive Multivariate Isolation (IMI) algorithm improves data reduction while maintaining physics performance.

## Key findings

- The IMI algorithm reduces data size by 45% while preserving full physics performance.
- IMI achieves 99% efficiency in selecting signal particles across diverse decay topologies.
- The algorithm is validated on real Run 3 data and shows robustness under actual conditions.

## 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.