Speaker
Description
Particle tracking is among the most sophisticated and complex parts of the full event reconstruction chain. Various reconstruction algorithms work in sequence to build trajectories from detector hits. Each of these algorithms requires numerous configuration parameters that need fine-tuning to properly account for the detector/experimental setup, the available CPU budget, and the desired physics performance. To automate and optimize the tuning of these parameters, automatic parameter optimization techniques were implemented in “A Common Tracking Software” (ACTS) framework, the open-source track reconstruction software framework. These techniques allow users to flexibly choose tunable parameters and define a cost/benefit function for optimizing the full reconstruction chain. Since their implementation, these techniques have been greatly beneficial for researchers across various experiments. This study discusses ongoing advancements in these optimization techniques, including novel approaches that enable a more systematic and gradual refinement of parameter tuning. Specifically, I will explore the integration of Bayesian Optimization techniques for tracking algorithm tuning, highlighting their potential to improve efficiency and precision beyond existing methods.