Speaker
Description
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. These identification algorithms will need to be adapted for the HL-LHC, particularly due to the presence of up to 200 simultaneous proton-proton interactions per event.. The Iterative CLustering (TICL) is currently being developed within the CMS collaboration. In this work, we present the application of a transformer-based neural network, ParticleTransformer, to identify hadronic tau decays in the Phase-2 HLT. This talk will also discuss the reconstruction quality of the tau decay products within the TICL framework, which serve as inputs to the network.