Performance Plots Showing the Effect of Different Cuts and Weighting in the Baseline Approach and a Technical Plot Showing the Effects of Pruning and Quantisation of the End-to-End Neural Network Approach to Phase-2 Level-1 Trigger Primary Vertex Reconstruction

The Phase-2 upgrade of the Level-1 (L1) Trigger will see tracker tracks reconstructed and passed down to global triggering algorithms such as primary vertex (PV) finding. This will be used to associate tracks and other trigger objects to the PV in an event, reducing the impact of pile-up (PU) and increasing trigger performance in certain scenarios. The baseline approach uses a $p_T$-weighted histogram of tracks in $z_0$ followed by a 3 bin convolution to find the peak. Different quality cuts can be imposed to reduce the impact of non-genuine (or tracks not originating from true charged particles) and have an impact on the baseline approach. Additionally, improvements to the baseline approach using extra track information are shown. The results presented here show the impact of various quality cuts and improvements to the baseline approach as well as additional plots to show the impact of pruning a neural network for the end-to-end approach. The plots shown are performed with 200K tt to semi-leptonic events.