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

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.

Institutions

Publication Details

Journal
The European Physical Journal C
Published
2026-03-09
DOI
https://doi.org/10.1140/epjc/s10052-026-15398-5
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

The European Physical Journal C
Particle Detector Development and Performance
article

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

article en

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.

The European Physical Journal CVol. 86(3)
Aix-Marseille Université (FR), University of Cambridge (GB), Australian Regenerative Medicine Institute (AU), University of Warwick (GB), École Polytechnique Fédérale de Lausanne (CH), Monash University (AU), University of Milano-Bicocca (IT), European Organization for Nuclear Research (CH), Syracuse University (US)
National Science Foundation, U.S. Department of Energy, CERN, UK Research and Innovation, University of Warwick, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Istituto Nazionale di Fisica Nucleare, Science and Technology Facilities Council
Openalex Percentile: Top 29%
Particle Detector Development and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.