Since 2015, LHCb’s central onboarding resource for new collaborators has been the Starterkit, a set of self-study lessons that also form the basis of an annual in-person workshop in Geneva. Ahead of Run 3 (2022–2026), a new version of the Starterkit was developed to accompany the Upgrade I software stack, with improved testing and updated exercises now used in the workshop. However, participation in the Geneva workshop is difficult for many Chinese collaborators due to distance, cost, and administrative constraints. To address this, the Starterkit content has been translated into Mandarin Chinese to support a corresponding workshop hosted within China. This is particularly important because China represents the largest group of LHCb collaborators outside Europe. Initial translations were produced using LLMs trained on LHCb documentation (based on LLaMA-derived models) and subsequently refined by students and reviewed by academics before going live to the website. Since then, updates to either language version are synchronised by a dedicated liaison who uses the same LLM tools to maintain consistency between the English and Chinese versions. To measure its popularity, basic analytics have been integrated into the lessons showing that approximately 25% of all Starterkit users access the Chinese translation. In this presentation the experiences around performing this translation are discusses as well as maintainance in the long-term to ensure synchronisation.

The LHCb experiment is planning a second major upgrade (Upgrade II) in the 2030s, with the goal of increasing the instantaneous luminosity to 1.0x1034 cm−2s−1. This upgrade aims to enhance the study of heavy flavor physics and to search for potential signals of new physics in the beauty and charm quark sectors. To operate under the demanding conditions of Upgrade II—characterized by higher radiation levels and significantly increased data rates—the LHCb detector will undergo substantial upgrades. For instance, the current Upstream Tracker (UT), which is based on silicon strip sensors, will be replaced by a new pixel sensor detector, known as the Upstream Pixel Tracker (UP). The primary function of the UP detector within the LHCb tracking system is to reduce the rate of ghost tracks and accelerate track reconstruction, particularly within the LHCb trigger system. It also enhances the reconstruction efficiency for long-lived particles such as KS0​ and Λ0. Currently, the detector is in the research and development (R&D) phase, with the goal of delivering the Upgrade II Technical Design Report (TDR) by 2026. In this presentation, I will first introduce the overall design of the UP detector, including the detector layout and the various pixel sensor chip options under consideration from different foundries. Following that, I will present simulation studies of the new UP detector and discuss its expected performance under the Upgrade II conditions.

LHCb - The LHCb experiment operates a full-software trigger comprising of two stages, labelled HLT1 and HLT2. The two stages are separated by a disk buffer, which not only allows the HLT2 processing to be asynchronous with respect to the data taking, it also allows real-time alignment and calibration to be performed prior to HLT2 processing. HLT2 then performs full offline-level reconstruction and applies complex physics selections across approximately 4,000 trigger lines. Maintaining and optimizing such a large menu of selections whilst respecting strict throughput and storage constraints is operationally challenging. Critically, the maximum allowed output rate of HLT1 is limited by the buffer size and the HLT2 average throughput. This poster presents two complementary efforts to reduce the processing cost of the HLT2 selection framework. First, the framework now automatically generates control-flow from existing data-flow descriptions, eliminating manual authoring overhead. This implementation is deliberately simple but effective, with earlier termination of lines and throughput improvements. Second, algorithmic overlap across the selection program was systematically characterized to understand duplicated processing and identify candidates for future optimization. Both efforts are validated against data using throughput benchmarks and checksums to verify data integrity. Results demonstrate throughput gains with preserved physics retention.

The LHCb experiment at the LHC employs a fully-software trigger to reconstruct and select events in real time. Key to this approach is the topological beauty (b) trigger, a set of algorithms which select decays of hadrons containing b quarks based on their distinct topology, i.e., highly displaced candidates with a large momentum. For Run 3 of the LHC, these algorithms were reimplemented using Lipschitz monotonic neural network (NN) architectures to provide robustness against varying detector conditions. Selected candidates are used across time-dependent analyses in LHCb, hence the selection must be efficient over and unbiased to the candidate lifetime. This becomes challenging in busy detector environments, wherein several decay processes are present, and the mis-association of constituent particles may result in background candidates with artificially high lifetimes. To this end, distance correlation and moment decomposition approaches to mitigate correlations between candidate lifetime and NN response have been studied and implemented in the LHCb trigger. This contribution motivates the use of these techniques, compares their respective merits, and discusses their implications for the performance of the LHCb topological b trigger.

High Energy Physics (HEP) experiments increasingly rely on large volumes of Monte Carlo (MC) simulation data to estimate radiation levels and activation scenarios. Within the LHCb collaboration, we present a new system developed to simplify the management and exploration of such MC simulation outputs as obtained with the FLUKA code: the Analysis platform for Radiation Environment Simulations (ARES). The platform consists of two complementary components. The first component, developed and maintained by the LHCb Glance project, provides a user-friendly interface for registering radiation estimators and associated metadata in a structured database, ensuring consistent organisation, traceability, and long-term reproducibility of simulation results. Integrated search and browsing functionalities enable users to efficiently navigate the stored datasets and quickly identify relevant estimators for their studies. Relying on Glance functionalities, this interface provides user management and access control based on roles and privacy levels within the LHCb collaboration. The second component serves as an independent, standalone plotting and analysis tool running on a dedicated server. This component connects seamlessly to the previously described system, allowing users to visualise, compare, and interpret simulation results directly within the web application. The interactive component also facilitates easy sharing of MC results within the collaboration, supporting more efficient collaborative workflows. While the current implementation focuses on LHCb use cases, extensions are planned to enable its use by other experiments and services. It may further be expanded to allow the upload and visualisation of radiation estimators obtained with other MC packages, providing a unified analysis environment. The presentation will describe the system architecture, its integration within LHCb, and outline the roadmap for future developments.

In the exabyte era, physical science research infrastructures will have to deal with massive quantities of raw data by relying on large heterogeneous computing facilities. In the LHCb context, the ODISSEE project aims to maximize the computational performance and reliability of those systems while reducing the required energy and the total cost of ownership by using AI tools and techniques. By leveraging the massive historical dataset of the LHCb Data Centre, it is possible to develop methods for optimizing data center cooling and for dynamically distributing computational tasks according to load requirements. The same monitoring information can be used to train a predictor of potential failures and to design a Digital Twin of the Data Centre. In this contribution, we show how AI can improve operations, efficiency, and sustainability of large-scale computing infrastructures in modern high-throughput physics experiments.

Pentaquark states, exotic hadronic states composed of five quarks, were first observed by LHCb in 2015 in the decay $\Lambda_b^0 \to p J/\psi K^-$, where three hidden-charm pentaquark candidates were identified. Based on their quark content, these states are also expected to manifest as resonances in the decays $\Lambda_b^0 \to \Lambda_c^+ \bar{D}^{*0} K^-$ and $\Lambda_b^0 \to \Sigma_c^+ \bar{D}^0 K^-$. Studying these channels can provide valuable insight into the internal structure and nature of the observed pentaquark states. In preparation for a Run~3 analysis of these decay modes, a kinematic over-constraint method is employed to reconstruct intermediate states, while neural-network-based classifiers are used to distinguish between the different decay channels. The methodology and preliminary results are presented in this poster.

We present a study of the charmless three-body decays $B^0_{(s)} \rightarrow K^0_S h^+ h^{\prime -}$, where $h$ is either a $\pi$ or a $K$, corresponding to six final states whose branching fractions are measured simultaneously. These decays are suppressed and, in certain cases, are dominated by penguin topologies. The data sample collected at LHCb between 2011 and 2018 is used for these measurements, corresponding to an integrated luminosity of $9~\mathrm{fb}^{-1}$. In a previous paper, using just Run~1 data, no conclusive evidence was found for the existence of $B^0_s \rightarrow K^0_S K^+ K^-$. With this larger dataset, $B^0_s \rightarrow K^0_S K^+ K^-$ is observed for the first time with a statistical significance corresponding to $8.6$ standard deviations. The poster will present the essential steps of the analysis. First, the selection procedure is outlined: a trigger, a cut-based preselection, and two boosted decision trees based on particle identification and on topological variables. Second, the fit strategy is described: separate fit components are included for the $B^0$ and $B^0_s$ signal peaks, partially reconstructed signal decays, crossfeed structures due to hadron misidentification, and combinatorial background. Third, the efficiency correction procedure is presented. This includes a description of the variation of efficiency across the Dalitz plane, and uses a data-driven estimate of the signal distribution in the Dalitz plane to obtain the weighted-average efficiency. Finally, the new branching fraction results are presented. Compared to the previous LHCb measurement, the uncertainties on the ratios of branching fractions are reduced by a factor of $2$ to $3$, due both to the larger data sample and to improvements in the analysis procedure. The tools developed for this branching fraction analysis are being reused in ongoing amplitude analyses of $B^0_{(s)} \rightarrow K^0_S h^+ h^{\prime -}$ decays.

Precise determinations of the CKM matrix element ∣Vub∣ are essential for testing the consistency of the Standard Model and for probing possible sources of flavour-changing new physics. LHCb offers a unique environment to study ∣Vub​∣ using semileptonic decays of various species of beauty hadrons, exploiting excellent vertexing, particle identification, and kinematic reconstruction. This contribution summarises the current status of ∣Vub∣ measurements at LHCb and discusses prospects for improved precision with larger data sets, refined analysis techniques, and improved theoretical inputs.

We present a study of the charmless three-body decays $B^{0}_{(s)} \to K^{0}_{S} h^{+} h^{\prime -}$, where $h$ is either a $\pi$ or a $K$, corresponding to six final states whose branching fractions are measured simultaneously. These decays are suppressed and, in certain cases, are dominated by penguin topologies. The data sample collected at \mbox{LHCb} between 2011 and 2018 is used for these measurements, corresponding to an integrated luminosity of $9~\mathrm{fb}^{-1}$. In a previous paper, using just Run~1 data, no conclusive evidence was found for the existence of $B^{0}_{s} \to K^{0}_{S} K^{+} K^{-}$. With this larger dataset, $B^{0}_{s} \to K^{0}_{S} K^{+} K^{-}$ is observed for the first time with a statistical significance corresponding to $8.6$ standard deviations. The poster will present the essential steps of the analysis. First, the selection procedure is outlined: a trigger, a cut-based preselection, and two boosted decision trees based on particle identification and on topological variables. Second, the fit strategy is described: separate fit components are included for the $B^{0}$ and $B^{0}_{s}$ signal peaks, partially reconstructed signal decays, crossfeed structures due to hadron misidentification, and combinatorial background. Third, the efficiency correction procedure is presented. This includes a description of the variation of efficiency across the Dalitz plane, and uses a data-driven estimate of the signal distribution in the Dalitz plane to obtain the weighted-average efficiency. Finally, the new branching-fraction results are presented. Compared to the previous \mbox{LHCb} measurement, the uncertainties on the ratios of branching fractions are reduced by a factor of $2$ to $3$, due both to the larger data sample and to improvements in the analysis procedure. The tools developed for this branching-fraction analysis are being reused in ongoing amplitude analyses of $B^{0}_{(s)} \to K^{0}_{S} h^{+} h^{\prime -}$ decays.