George Hallett | Search arXiv

arXiv2026

Translating LHCb's Documentation: First experiences and ensuring maintenance

The Starterkit Lessons online and Starterkit Workshops held in Geneva each year have been the main method of onboarding newcomers to the LHCb experiment since its founding in 2015. The new software corresponding to Upgrade 1 of the LHCb and Run 3 of datataking at the LHC has necessitated a new version of this Starterkit to be written. This new version has made several improvements over the old one, with increased maintainability through testing of examples in CI pipelines, and has also allowed for translations of the Starterkit Lessons. In late 2025 the Run 3 Starterkit lessons were translated into Mandarin Chinese, to allow for a sibling Starterkit event to take place in China, with a dedicated liaison role set up to ensure synchronization between the two translations. To measure the success of the Chinese Starterkit Lessons, analytics have been added revealing that roughly 22% of all visits to the Starterkit website will access at least one Chinese-language page.

physics.ed-ph↗

arXiv2026

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.

hep-ex↗