A novel combination of established data analysis techniques is proposed for the reconstruction of all tracks of primary charged particles created in high energy collisions. It uses all information available in a collision event while keeping competing choices open for as long as possible. Suitable track candidates are selected by transforming measured hits to a binned, three- or four-dimensional, track parameter space. The transformation is based on templates, taking advantage of the translational and rotational symmetries of a given detector. Subsequently, their number is further narrowed down by a Kalman filter-based technique. Track candidates and their corresponding hits form a highly connected network, a bipartite graph, where one allows for multiple assignments of hits to track candidates. The graph is cut into very many minigraphs by removing a few of its components. Finally, the hits are distributed among the track candidates by exploring a deterministic decision tree. A depth-limited search is performed, maximising the number of hits on tracks and minimising the sum of track-fit χ2. Simplified models of LHC silicon trackers, as well as the relevant physics processes, are employed to study the performance (efficiency, purity, timing) of the proposed method in the case of single or many simultaneous proton-proton collisions (so-called event pile-up), and for single heavy-ion collisions at the highest available energies.
ASJC Scopus subject areas
- Nuclear and High Energy Physics