Recent advancements in the tau reconstruction and identification techniques in CMS

TL;DR

This paper reviews recent advancements in tau lepton reconstruction and identification techniques at CMS, highlighting deep learning algorithms, their calibration, and alternative approaches for improved physics analysis at the LHC.

Contribution

It introduces novel deep learning-based algorithms for tau identification, including domain adaptation and graph neural networks, with demonstrated performance improvements.

Findings

Deep convolutional neural network improves tau discrimination.

Algorithms calibrated successfully with early Run 3 data.

Graph neural networks enable identification of displaced tau leptons.

Abstract

Tau leptons play a crucial role in studies of the Higgs boson and searches for Beyond the Standard Model physics at the present LHC and in its high luminosity upgrade. This talk presents the latest advancements in the reconstruction and identification of hadronic decays of tau leptons at the CMS experiment, both at the online and offline levels. The tau identification algorithm deployed for the early Run 3 data-taking period, based on a deep convolutional neural network with domain adaptation, showcases significantly improved discrimination of genuine hadronic tau decays against mis-identified quark and gluon jets, electrons, and muons. During live data-taking, a simplified version of the algorithm is used to select events with tau leptons at the High Level Trigger (HLT). The performance and calibration of both algorithms using early Run 3 data are presented. Many CMS physics analyses involving tau leptons are expected to benefit from these improvements. Alternative approaches to identify hadronic taus combined with jet flavour, based on graph neural networks and particle transformers, are also covered. Additionally, the dedicated techniques used to reconstruct and identify displaced tau leptons originating from long-lived particle decays using graph neural networks are discussed.