Speaker
Description
Present data taking conditions and further
upgrades of high energy particle colliders, as well as detector systems, call for new ideas. A novel combination of
established data analysis techniques for charged-particle reconstruction 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
usually form a highly connected network, a bipartite graph, where we allow for
multiple hit to track assignments. In order to get a manageable problem, the
graph is cut into very many subgraphs by removing a few of its vulnerable
components, edges and nodes ($k$-connectivity). Finally the hits of a subgraph
are distributed among the track candidates by exploring a deterministic
single-player game tree. A depth-limited search is performed with a sliding
horizon maximizing the number of hits on tracks, and minimizing the sum of track-fit
$\chi^2$.
Simplified but realistic models of LHC silicon trackers including the relevant
physics processes are used to test and study the performance (efficiency,
purity, timing, paralellisation) of the proposed method in the case of numerous simultaneous
proton-proton collisions (high pileup), and for single ion-ion collisions at
the highest available energies.
