Nuclear Physics Division

Heavy-ion collisions provide a unique opportunity to study the quark-gluon plasma (QGP), an exotic state of matter where quarks and gluons are no longer confined within hadrons and believed to have existed just a few microseconds after the Big Bang. Charm quarks are among the key probes for investigating the QGP. Indeed, they retain information about their interactions with the QGP, making them essential for understanding the properties of the plasma. The production of charm quarks and their interactions with the QGP is studied through the measurements of hadrons, mesons and baryons, containing at least one charm quark or antiquark, like D0 mesons or Lambda_c baryons. However, the hadronization process—how charm quarks become confined within colorless baryons or mesons—remains poorly understood.

A promising approach to gaining deeper insights into charm hadronization is to measure the elliptic flow of charm hadrons, which refers to long-range angular correlations and is a signature of collective effects due to thermalization. By comparing the elliptic flow of D0 mesons and Lambda_c baryons, researchers can better understand the charm hadronization mechanism, which is sensitive to the properties of the created medium.

To measure elliptic flow, the selected student will develop an innovative method that leverages the full capabilities of the detector. This method, which has never been applied before, provides a more intuitive and theoretically sound interpretation of the results. The candidate will adapt this technique for use with the LHCb detector to measure, compare, and interpret the elliptic flow of Lambda_c charm baryons and D0 mesons with the PbPb samples collected by LHCb in 2024.

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.