Abstract
A novel combination of established data analysis techniques for reconstructing charged-particles in high energy collisions is proposed. It uses all information available in a collision event while keeping competing choices open as long as possible. Suitable track candidates are selected by transforming measured hits to a binned, three- or four-dimensional, track parameter space. It is accomplished by the use of templates taking advantage of the translational and rotational symmetries of the detectors. Track candidates and their corresponding hits, the nodes, form a usually highly connected network, a bipartite graph, where we allow for multiple hit to track assignments, edges. In order to get a manageable problem, the graph is cut into very many minigraphs by removing a few of its vulnerable components, edges and nodes. Finally the hits are distributed among the track candidates by exploring a deterministic decision tree. A depth-limited search is performed maximizing the number of hits on tracks, and minimizing the sum of track-fit χ 2 . Simplified but realistic models of LHC silicon trackers including the relevant physics processes are used to test and study the performance (efficiency, purity, timing) of the proposed method in the case of single or many simultaneous proton-proton collisions (high pileup), and for single heavy-ion collisions at the highest available energies.
Highlights
Traditional methods of track reconstruction can be scaled to work in high multiplicity events, namely in many simultaneous collisions of elementary particles [1, 2] and in high multiplicity single heavy-ion collisions
In the case of silicon trackers the combinatorial track finding methods employed for trajectory building mostly use local information [8, 9]
The graph is partitioned into very many minigraphs by removing a few of its vulnerable components, edges and nodes
Summary
Traditional methods of track reconstruction can be scaled to work in high multiplicity events, namely in many simultaneous collisions (pileup) of elementary particles [1, 2] and in high multiplicity single heavy-ion collisions. In the case of silicon trackers the combinatorial track finding methods employed for trajectory building mostly use local information [8, 9]. They start with a trajectory seed and build a trajectory by extending the seed through the detector layers, picking up compatible hits. In the case of very many compatible hits the number of concurrently built trajectory candidates must be limited. Some of the best candidates are kept which biases the final result. In this sense decisions are made too early. Trajectories are mostly treated separately, there is no interaction between their assigned hits
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