Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s} =$ 13 TeV

TL;DR

This paper evaluates the performance of the upgraded CMS Level-1 trigger during Run 2 of the LHC, highlighting improvements in event selection efficiency and background reduction under high-luminosity conditions.

Contribution

It introduces new pattern recognition, machine learning techniques, and pileup mitigation methods in the CMS Level-1 trigger, enhancing physics signal detection during high-luminosity runs.

Findings

Improved trigger efficiency for various physics signals.

Significant reduction in background trigger rates.

Enhanced pileup handling capabilities.

Abstract

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13 TeV. During Run 2 (years 2015-2018) the LHC eventually reached a luminosity of 2.1 $\times$ 10$^{34}$ cm$^{-2}$ s$^{-1}$, almost three times that reached during Run 1 (2009-2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016-2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals.