Speaker
Salmaan A Barday
(UCT)
Description
Accurate track matching is vital for reconstructing particle trajectories
in high-energy physics. In ALICE at the LHC, the upgraded muon tracking
system combines data from the Muon Spectrometer with the new Muon
Forward Tracker (MFT), a highly segmented silicon pixel detector positioned
near the interaction point before the hadron absorber. With the
MFT recording orders of magnitude more tracks than the spectrometer, we
developed a refined machine learning-based matching method trained on
Monte Carlo data. A subsequent data-driven approach will be explored to
address potential limitations of Monte Carlo training. These enhancements
aim to improve muon track reconstruction in ALICE, thereby supporting
more precise physics analyses.
| Apply for student award at which level: | MSc |
|---|---|
| Consent on use of personal information: Abstract Submission | Yes, I ACCEPT |
Author
Salmaan A Barday
(UCT)
