A Multi-Objective Optimization Tool for Track Reconstruction in CMS

Efficient and precise track reconstruction is critical for the results of the Compact Muon Solenoid (CMS) experiment. The current CMS track reconstruction algorithm is a multi-step procedure that consists in a Cellular Automaton technique to create track seeds, followed by Kalman filter based methods to build the trajectory pattern and final fit. Multiple parameters regulate the reconstruction steps, populating a large phase space of possible solutions. The fine-tuning of these parameters is necessary to ensure an optimal reconstruction. This report presents an original tool based on the established Particle Swarm heuristic optimization algorithm (PSO) to perform parameter tuning of the pixel track reconstruction software. The software enables Multi-Objective Optimization against tracking efficiency and fake rate, resulting in the individuation of a Pareto front of valid parameters’ sets for reconstruction. The algorithm has been tested at the end of the data-taking period of 2023 with excellent results. The parameters obtained with the optimization resulted in comparable reconstruction’s efficiency with a 50% reduction in misidentified tracks, especially for low transverse momentum of the particles.