Pattern recognition with vector hits

Abstract

Trackers at the future high-luminosity LHC, designed to have triggering capability, will feature layers of stacked modules with a small stack separation. This will allow the reconstruction of track stubs or vector hits with position and direction information, but lacking precise curvature information. This opens up new possibilities for track finding, online and offline. Two track finding methods, the Kalman filter and the convergent Hough transform are studied in this context. Results from a simplified fast simulation are presented. It is shown that the performance of the methods depends to a large extent on the size of the stack separation. We conclude that the detector design and the choice of the track finding algorithm(s) are strongly coupled and should proceed conjointly.