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