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

Area of Science:

• High-energy particle physics
• Detector data processing
• Computational physics

Background:

• The Large Hadron Collider (LHC) Run 3 generates massive data volumes, challenging detector event reconstruction.
• Charged particle information constitutes the majority of event size, necessitating data reduction.
• Heavy-flavour physics programs require efficient event selection without compromising physics reach.

Purpose of the Study:

• To develop and validate a novel data reduction technique for LHCb.
• To address the data size imbalance caused by high particle multiplicity in LHC collisions.
• To improve the efficiency and robustness of event reconstruction for heavy-flavour physics.

Main Methods:

• Development of a suite of inclusive isolation tools, including classical methods and a novel Inclusive Multivariate Isolation (IMI) algorithm.
• The IMI algorithm unifies strengths of traditional isolation techniques for robust handling of diverse decay topologies and kinematics.
• Validation of the IMI algorithm on Run 3 LHCb data under real conditions.

Main Results:

• The IMI algorithm achieves a 45% reduction in event data size by retaining only essential particles.
• Signal particles are selected with 99% efficiency, outperforming traditional methods in background rejection.
• The IMI demonstrates superior performance across diverse decay channels and event multiplicities.

Conclusions:

• The IMI algorithm is a robust and efficient method for reducing event data size in high-energy physics experiments.
• It preserves essential physics information, enabling effective heavy-flavour physics analysis.
• IMI offers a potential long-term solution for data selection in the high-luminosity LHC environment.