  Particle tracking is among the most sophisticated and complex part of the
full event reconstruction chain. A number of reconstruction algorithms work in
a sequence to build these trajectories from detector hits. These algorithms use
many configuration parameters that need to be fine-tuned to properly account
for the detector/experimental setup, the available CPU budget and the desired
physics performance. The most popular method to tune these parameters is
hand-tuning using brute-force techniques. These techniques can be inefficient
and raise issues for the long-term maintainability of such algorithms. The
open-source track reconstruction software framework known as "A Common Tracking
Framework (ACTS)" offers an alternative solution to these parameter tuning
techniques through the use of automatic parameter optimization algorithms. ACTS
comes equipped with an auto-tuning suite that provides necessary setup for
performing optimization of input parameters belonging to track reconstruction
algorithms. The user can choose the tunable parameters in a flexible way and
define a cost/benefit function for optimizing the full reconstruction chain.
The fast execution speed of ACTS allows the user to run several iterations of
optimization within a reasonable time bracket. The performance of these
optimizers has been demonstrated on different track reconstruction algorithms
such as trajectory seed reconstruction and selection, particle vertex
reconstruction and generation of simplified material map, and on different
detector geometries such as Generic Detector and Open Data Detector (ODD). We
aim to bring this approach to all aspects of trajectory reconstruction by
having a more flexible integration of tunable parameters within ACTS.
