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Abstract

The ALICE experiment at the CERN LHC identifies D0 mesons via secondary-vertex reconstruction and topological cuts to reduce the corresponding combinatorial background in heavy-ion collisions. The D0 meson is produced promptly in initial, hard scatterings via the strong interaction or as feed-down from weakly decaying B hadrons. Within this thesis, a novel method for the separation of prompt and feed-down D0 mesons using cut variations was implemented and applied to data from p–Pb collisions at sqrt(s_NN) = 5.02 TeV. The effectiveness of the secondary-vertex reconstruction strongly depends on the performance and in particular the pointing resolution of the Inner Tracking System. The upgrade of the ALICE Inner Tracking System for the Long Shutdown 2 of the LHC in 2019/2020 will significantly improve its vertex-reconstruction and tracking capabilities. It will be equipped with Monolithic Active Pixel Sensors manufactured using the TowerJazz 180nm CMOS process on wafers with a high-resistivity epitaxial layer. In another part of this thesis, several pixel-chip prototypes of the ALPIDE architecture with in-pixel amplification and discrimination as well as in-matrix data reduction were characterised. The pALPIDE-2 prototype was measured to fulfil the requirements in terms of detection efficiency, fake-hit rate, position resolution and tolerance to irradiation with non-ionising energy loss. Based on simulations modelling the tracking and vertex-reconstruction performance of the upgraded Inner Tracking System, the perspective of the feed-down separation using cut variations after the upgrade was assessed within this thesis.