Speaker
Description
A novel combination of data analysis techniques is proposed for the
reconstruction of all tracks of primary charged particles, as well as of
daughters of displaced vertices (decays, photon conversions, nuclear
interactions), created in high energy collisions. Instead of performing a
classical trajectory building or an image transformation, an efficient use of
both local and global information is undertaken while keeping competing choices
open.
The measured hits of adjacent tracking layers are clustered first with help of
a mutual nearest neighbor search in angular distance. The resulted chains of
connected hits are used as initial clusters and as input for cluster analysis
algorithms, such as the robust $k$-medians clustering. This latter proceeds by
alternating between the hit-to-track assignment and the track-fit update steps,
until convergence. The calculation of the hit-to-track distance and that of the
track-fit $\chi^2$ is performed through the global covariance of the measured
hits. The clustering is complemented with elements from a more sophisticated
Metropolis-Hastings MCMC algorithm, with the possibility of adding new track
hypotheses or removing unnecessary ones.
Simplified but realistic models of today's silicon trackers, including the
relevant physics processes, are employed to test and study the performance
(efficiency, purity) of the proposed method as a function of the particle
multiplicity in the collision event.
