This PhD aims to develop and optimize next-generation track reconstruction capabilities for the LHCb experiment at the Large Hadron Collider (LHC) through the exploration of advanced machine learning (ML) algorithms. The newly installed Upstream Pixel (UP) detector, located upstream of the LHCb magnet, will play a crucial role from Run 5 onward by rapidly identifying track candidates and reducing fake tracks at the earliest stages of reconstruction, particularly in high-occupancy environments.

Achieving fast and highly efficient tracking is essential to fulfill LHCb’s rich physics program, which spans rare decays, CP-violation studies in the Standard Model, and the characterization of the quark–gluon plasma in nucleus–nucleus collisions. However, the increasing event rates and data complexity expected for future data-taking phases will impose major constraints on current tracking algorithms, especially in heavy-ion collisions where thousands of charged particles may be produced per event.

In this context, we will investigate modern ML-based approaches for standalone tracking in the UP detector. Successful applications in the LHCb VELO tracking system already demonstrate the potential of such methods. In particular, Graph Neural Networks (GNNs) are a promising solution for exploiting the geometric correlations between detector hits, allowing for improved tracking efficiency and fake-rate suppression, while maintaining scalability at high multiplicity.

The PhD program will first focus on the development of a realistic GEANT4 simulation of the UP detector to generate ML-suitable datasets and study detector performance. The next phase will consist in designing, training, and benchmarking advanced ML algorithms for standalone tracking, followed by their optimization for real-time GPU-based execution within the Allen trigger and reconstruction framework. The most efficient solutions will be integrated and validated inside the official LHCb software stack, ensuring compatibility with existing data pipelines and direct applicability to Run-5 operation.

Overall, the thesis will provide a major contribution to the real-time reconstruction performance of LHCb, preparing the experiment for the challenges of future high-luminosity and heavy-ion running.