The increase in luminosity during the LHC upgrade programme causes a challenging rise in track multiplicity and hit occupancy in the LHCb detector. In order to mitigate this effect, the use of photon detector hit time information is presented in the context of the Ring-Imaging Cherenkov (RICH) detectors. The application of a time gate in the FPGA of the digital readout board for the Upgrade Ia photon detector, which is being installed for LHC Run 3, is described. Data recorded during SPS charged particle beam tests using a 6.25 ns time gate show a reduction of up to a factor of four in asynchronous detector noise compared to the original 25ns readout. A time-walk correction based on the time-over- threshold is proposed. Using the LHCb simulation framework, the intrinsic time resolution of the RICH detectors is demonstrated to be less than 10 ps. This is particularly relevant for the LHCb Upgrade II, which is scheduled for the year 2030 in preparation for a further order- of-magnitude rise in luminosity. Methods of time gating and scaling of the signal amplitude in the RICH reconstruction likelihood maximisation algorithm are presented. The results show that, considering improvements in the time-resolution only, a photon detector with an approximately 50 ps resolution can achieve today’s particle ID performance in the high- luminosity LHC environment.

In the second part of this thesis, the first published semiconductor tracker for cosmic-ray muon scattering tomography is presented. The tracker uses silicon strip sensors from the ATLAS Semiconductor Tracker (SCT) with an 80μm pitch. A novel electronic readout system for the sensors is designed, based on a scalable, inexpensive, flexible, FPGA-based solution. A high-precision mechanical structure with integrated cooling is built to align the SCT modules. This alignment is fine-tuned in software, and the tracker performance is compared with a Geant4 simulation. A scattering angle resolution compatible with 1.5 mrad at the 4 GeV average cosmic-ray muon energy is obtained. Data are recorded for plastic, iron and lead samples using 45000 muons. Images are reconstructed using the Angle Statistics Reconstruction algorithm, and demonstrate good contrast between low and high atomic number materials.