High-level hadronic tau lepton triggers of the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

TL;DR

This paper describes the implementation and performance of machine-learning based high-level tau lepton triggers in the CMS experiment, optimized for increased collision rates at 13.6 TeV.

Contribution

Introduction of efficient machine-learning algorithms for tau lepton identification at the trigger level in CMS during high-luminosity conditions.

Findings

High identification efficiency achieved for hadronic tau decays.

Low computational cost of the new trigger algorithms.

Successful deployment in 2022-2023 CMS data collection.

Abstract

The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of interactions per bunch crossing in recent years. To address this challenge, a series of machine-learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022$-$2023, corresponding to an integrated luminosity of 62 fb$^{-1}$.