In the field of High Energy Physics, we are entering an era marked by high precision measurements and immense discovery potential. This progress is driven by increased energy and luminosity at the forthcoming High-Luminosity LHC (HL-LHC) and other future colliders. These advancements present unprecedented opportunities for exploring fundamental physics while posing significant challenges, particularly in particle track reconstruction. The higher collision rates at the HL-LHC result in increased track density, leading to challenges such as higher pile-up, increased detector occupancy, and the difficulty of distinguishing individual particle trajectories. Effective track reconstruction is essential for the accurate interpretation of events, including the identification of primary and secondary vertices, and precise measurements of particle’s momentum and energy.
This presentation focuses on two core projects aimed at addressing these challenges: the development of deep neural networks (DNNs) to precisely identify and locate primary vertices in collision data, and the implementation of automated parameter optimization techniques to improve tracking algorithm performance. Both approaches aim to meet the demands of the high-luminosity environment.
Additionally, I will touch briefly on the advancements in future collider designs, particularly the Muon Collider. Its unique collision environment, characterized by beam instability from muon decays, poses distinct challenges to particle tracking. I will discuss ongoing efforts to optimize tracking algorithms to mitigate the effects of beam-induced backgrounds, ensuring robust performance in these novel experimental conditions.
Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/98973156241?pwd=cEU5RFdlVXoyc0JTeTlDMkozKzQ5UT09
Jamie Ryan (jlryan@slac), Zhi Zheng (zzheng@slac)
