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Particle track reconstruction, in which the trajectories of charged particlesare determined, is a critical and time consuming component of the full eventreconstruction chain. The underlying software is complex and consists of anumber of mathematically intense algorithms, each dealing with a particulartracking sub-process. These algorithms have many input parameters that need tobe supplied in advance. However, it is difficult to determine the configurationof these parameters that produces the best performance. Currently, the inputparameter values are decided on the basis of prior experience or by the use ofbrute force techniques. A parameter optimization approach that is able toautomatically tune these parameters for high performance is greatly desirable.In the current work, we explore various machine learning based optimizationmethods to devise a suitable technique to optimize parameters in the complextrack reconstruction environment. These methods are evaluated on the basis of ametric that targets high efficiency while keeping the duplicate and fake ratessmall. We focus on derivative free optimization approaches that can be appliedto problems involving non-differentiable loss functions. For our studies, weconsider the tracking algorithms defined within A Common Tracking Software(ACTS) framework. We test our methods using simulated data from ACTS softwarecorresponding to the ACTS Generic detector and the ATLAS ITk detectorgeometries.

Exploration of different parameter optimization algorithms within the context of ACTS software framework