This proposal focuses on enhancing tracking performance for the LHCb experiments during Run 5 at the Large Hadron Collider (LHC) through the exploration of various machine learning-based algorithms. The Upstream Tracker (UT) sub-detector, a crucial component of the LHCb tracking system, plays a vital role in reducing the fake track rate by filtering out incorrectly reconstructed tracks early in the reconstruction process. As the LHCb detector investigates rare particle decays, studies CP violation in the Standard Model, and study the Quark-Gluon plasma in PbPb collisions, precise tracking becomes increasingly important.

With upcoming upgrades planned for 2035 and the anticipated increase in data rates, traditional tracking methods may struggle to meet the computational demands, especially in nucleus-nucleus collisions where thousands of particles are produced. Our project will investigate a range of machine learning techniques, including those already demonstrated in the LHCb’s Vertex Locator (VELO), to enhance the tracking performance of the UT. By applying diverse methods, we aim to improve early-stage track reconstruction, increase efficiency, and decrease the fake track rate. Among these techniques, Graph Neural Networks (GNNs) are a particularly promising option, as they can exploit spatial and temporal correlations in detector hits to improve tracking accuracy and reduce computational burdens.

This exploration of new methods will involve development work tailored to the specific hardware selected for deployment, whether it be GPUs, CPUs, or FPGAs, all part of the futur LHCb’s data architecture. We will benchmark these algorithms against current tracking methods to quantify improvements in performance, scalability, and computational efficiency. Additionally, we plan to integrate the most effective algorithms into the LHCb software framework to ensure compatibility with existing data pipelines.