Licentiate thesis: Instrumentation for silicon tracking at the HL-LHC
Wednesday 14 June 2017
to 11:30 at
Rebecca Carney (Stockholm University, Department of Physics)
In 2027 the Large Hadron Collider (LHC) at CERN will enter a high luminosity phase, delivering 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 period. This will impose significant technical
challenges on all aspects of the ATLAS detector but particularly the Inner Detector, trigger, and data acquisition
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 dealing with the conditions in HLLHC,
an inter-experiment collaboration called RD53 has been formed. RD53 is tasked with producing a frontend
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 effects 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 present a challenge for the current track reconstruction 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 approaches 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.