Instrumentation for silicon tracking at the HL-LHC

In 2027 the Large Hadron Collider (LHC) at CERN will enter a high luminosity phase, deliver- ing 3000 fb 1 over the course of ten years. The High Luminosity LHC (HL-LHC) will increase the instantaneous luminosity delivered by a factor of 5 compared to the current operation pe- riod. This will impose significant technical challenges on all aspects of the ATLAS detector but particularly the Inner Detector, trigger, and data acquisition systems. In addition, many of the components of the Inner Detector are reaching the end of their designed lifetime and will need to be exchanged. As such, the Inner Detector will be entirely replaced by an all silicon tracker, known as the Inner Tracker (ITk). The layout of the Pixel and strip detectors will be optimised for the upgrade and will extend their forward coverage. To reduce the per-pixel hit rate and explore novel techniques for deal- ing with the conditions in HL-LHC, an inter-experiment collaboration called RD53 has been formed. RD53 is tasked with producing a front-end readout chip to be used as part of hybrid Pixel detectors that can deal with the high multiplicity environment in the HL-LHC. A silicon sensor, which makes up the other half of the hybrid Pixel detector, must also be designed to cope with the high fluences in HL-LHC. Significant damage will be caused by non- ionising energy loss in the sensor over its lifetime. This damage must be incorporated into the detector simulation both to predict the detector performance at specific conditions and to understand the e↵ects of radiation damage on data taking. The implementation of radiation damage in the ATLAS simulation framework is discussed in this thesis. Collisions produced by the HL-LHC also presents a challenge for the current track reconstruc- tion software. High luminosity is obtained, in part, by increasing the number of interactions per bunch crossing, which in turn increases the time taken for track reconstruction. Various ap- proaches to circumvent the strain on projected resources are being explored, including porting existing algorithms to parallel architectures. A popular algorithm used in track reconstruction, the Kalman filter, has been implemented in a neuromorphic architecture: IBM’s TrueNorth. The limits of using such an architecture for tracking, as well as how its performance compares to a non-spiking Kalman filter implementation, are explored in this thesis.