Abstract
A novel combination of data analysis techniques is introduced for the reconstruction of primary charged particles and of daughters of photon conversions, created in high energy collisions. Instead of performing a classical trajectory building or an image transformation, efficient use of both local and global information is undertaken while keeping competing choices open. The measured hits in silicon-based tracking detectors are clustered with the help of a k-medians clustering. It proceeds by alternating between the hit-to-track assignment and the track-fit update steps, until convergence. The clustering is complemented with the possibility of adding new track hypotheses or removing unnecessary ones. A simplified model of a silicon tracker is employed to test the performance of the proposed method, showing good efficiency and purity characteristics.
Highlights
The reconstruction of charged particles, of their trajectories, is an active area of research in high energy particle and nuclear physics
A novel combination of data analysis techniques was proposed for the reconstruction of all tracks of primary charged particles, as well as of daughters of displaced vertices, 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 was undertaken while keeping competing choices open
Summary
The reconstruction of charged particles, of their trajectories, is an active area of research in high energy particle and nuclear physics. Detectors at today’s particle colliders mostly employ large surface silicon-based tracking devices, which sample the trajectory of the emitted charged particles at several locations. When a charged particle crosses the semiconducting material, it deposits energy and creates a hit by exciting electrons to the valence band, producing electron-hole pairs. The silicon-based trackers are highly segmented; they consist of several millions of tiny pixels (dimensions of ∼100 μm) and of narrow, but long strips (∼10 cm in length). In a high energy collision event, several thousands of pixel and strip hits are created. Our task is to solve a mathematical puzzle: the goal is to identify particle trajectories by associating most of these hits with a limited number of true trajectories. The default solution for this problem is the combinatorial track finding and fitting [1]
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