With the increasing centre-of-mass energy and luminosity of the Large Hadron Collider
(LHC), the Compact Muon Experiment (CMS) is undertaking upgrades to its triggering system
in order to maintain its data-taking efficiency. In 2016, the Phase-1 upgrade to the CMS Level-
1 Trigger (L1T) was commissioned which required the development of tools for validation of
changes to the trigger algorithm firmware and for ongoing monitoring of the trigger system
during data-taking. A Phase-2 upgrade to the CMS L1T is currently underway, in preparation
for the High-Luminosity upgrade of the LHC (HL-LHC). The HL-LHC environment is expected
to be particularly challenging for the CMS L1T due to the increased number of simultaneous
interactions per bunch crossing, known as pileup. In order to mitigate the effect of pileup, the
CMS Phase-2 Outer Tracker is being upgraded with capabilities which will allow it to provide
tracks to the L1T for the first time.
A key to mitigating pileup is the ability to identify the location and decay products of the signal
vertex in each event. For this purpose, two conventional algorithms have been investigated, with
a baseline being proposed and demonstrated in FPGA hardware. To extend and complement the
baseline vertexing algorithm, Machine Learning techniques were used to evaluate how different
track parameters can be included in the vertex reconstruction process. This work culminated
in the creation of a deep convolutional neural network, capable of both position reconstruction
and association through the intermediate storage of tracks into a z histogram where the optimal
weighting of each track can be learned. The position reconstruction part of this end-to-end model
was implemented and when compared to the baseline algorithm, a 30% improvement on the
vertex position resolution in tt̄ events was observed.Open Acces
