Speaker
Description
Accurate and efficient track reconstruction is critical for the results of high-energy physics experiments, particularly as upcoming high-luminosity environments become more complex with increased data rates and pile-up. Reconstruction algorithms depend on numerous parameters whose optimal configuration is crucial and non-trivial. Manual tuning in such high-dimensional solution spaces is often not enough.
Here, we present The Optimizer, a flexible software framework that leverages heuristic optimization techniques to automate the tuning of track reconstruction parameters. The tool enables the exploration of a Pareto front of solutions that optimize key performance metrics such as tracking efficiency and fake rate. Designed to be agnostic to the reconstruction software, The Optimizer allows for integration with various pipelines and supports extensions to other optimization techniques.
The framework has also been tested with the standalone open-source version of the CMS pixel track reconstruction software, using both CMS open data and the TrackML simulated dataset. The tests results and the validation with standard benchmark test functions for multi-objective optimization demonstrates the tool's potential to improve the performance of tracking systems under increasingly demanding experimental conditions.