Exploring new directions in enhancing the ACTS parameter optimization suite

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.

The current study discusses recent developments in these optimization techniques. It explores novel optimization methods that provide a more gradual and systematic approach to parameter tuning. Specifically, we have investigated the potential application of gradient based techniques for tuning tracking algorithm parameters and discussed the improvements achieved over the original techniques.