Tau Lepton Identification Research Articles

Tau lepton identification and reconstruction: A new frontier for jet-tagging ML algorithms

Identifying and reconstructing hadronic τ decays (τh) is an important task at current and future high-energy physics experiments, as τh represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of τh can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful τh identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating τh from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art τh reconstruction and identification algorithms currently in use at the Large Hadron Collider.

Tau Lepton Identification With Graph Neural Networks at Future Electron–Positron Colliders

Efficient and accurate reconstruction and identification of tau lepton decays plays a crucial role in the program of measurements and searches under the study for the future high-energy particle colliders. Leveraging recent advances in machine learning algorithms, which have dramatically improved the state of the art in visual object recognition, we have developed novel tau identification methods that are able to classify tau decays in leptons and hadrons and to discriminate them against QCD jets. We present the methodology and the results of the application at the interesting use case of the IDEA dual-readout calorimeter detector concept proposed for the future FCC-ee electron–positron collider.

Tau Identification With Deep Neural Networks at the CMS Experiment

The reconstruction and identification of tau leptons decaying to hadrons are crucial for new physics signatures and precision measurements with tau leptons in the final state at the LHC. The recently deployed tau identification algorithm using deep neural network (DNN) at the CMS experiment for the discrimination of hadronic tau decays from quark or gluon induced jets, electrons, or muons is an ideal example for the exploitation of modern deep learning neural network techniques in high energy physics. With this algorithm, significant suppression of tau misidentification rates has been achieved for the same identification efficiency compared to previous algorithms at the LHC, leading to considerable performance gains for physics studies with tau leptons. This new multiclass DNN-based tau identification algorithm at CMS and its performance are presented in this article.

Reconstruction and identification of tau leptons in CMS

Tau leptons constitute an important experimental signature for analyses at the CERN LHC related to Higgs boson, Standard Model, and beyond the Standard Model measurements. We describe the algorithm used by the CMS experiment to reconstruct and identify decays of tau leptons into hadrons and a neutrino during Run 1 of the CERN LHC. The performance of the algorithm is studied in proton-proton collisions recorded at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb −1 . The algorithm achieves an identification efficiency of typically 50-60%, with misidentification rates for quark and gluon jets, electrons and muons that vary between per mille and percent levels.

Reconstruction and Identification of Tau Leptons in ATLAS

These proceedings describe the reconstruction and identification of tau leptons in the ATLAS experiment at the LHC. Tau leptons play an important role in the measurement of Standard Model processes, in the search for new physics (Supersymmetry, heavy resonances) and in the measurement of Higgs boson properties. Hadronic tau lepton decays are reconstructed calorimeter based and separation against QCD-induced jets is done relying on shapes of energy depositions in the calorimeter as well as tracking based variables. The energy scale of tau leptons is obtained by scaling the measured momentum to the simulated one and is cross-checked in data. The tau trigger makes use of the collimated nature of tau decays and relies on a combination of objects (e.g. tau + muon) to reduce the rates and thresholds. An outlook to the improved tau reconstruction to be used in the next data-taking period of ATLAS concludes the proceedings.

CLIC vertex detector R&D

In order to achieve its primary objectives of heavy-flavour tagging and tau lepton identification, the CLIC vertex detector must precisely reconstruct displaced vertices. This requires accurate determination of the impact parameter and charge of tracks originating from the secondary vertex. Excellent spatial resolution must therefore be provided down to low polar angles, whilst maintaining low occupancy, low mass and low power dissipation. These requirements challenge current technological limits, and demand a broad programme of R&D. A detector concept is currently under development, comprising a hybrid pixel detector of small-pitch readout ASICs implemented in 65nm CMOS technology (CLICpix) combined with ultra-thin sensors. The readout chips are low-power, and power-pulsing is used to reduce further their power dissipation. This enables a forced gas cooling system in the vertex detector region. In this paper, the CLIC vertex detector requirements are reviewed and the current status of R&D on sensors, readout, powering, cooling and supports is presented.

Physics with $\tau$ Leptons in ATLAS

Physics involving tau lepton signatures form an integral part of the ATLAS physics program. The tau lepton identification is used in many results at ATLAS, from searches for exotic phenomena and Higgs bosons, along with Standard Model of cross section measurements. This paper reviews the performance of the reconstruction and identification algorithms for hadronically decaying tau leptons. In addition, cross sections of Standard Model processes involving tau decays are presented. Searches for Higgs bosons and other phenomena beyond the Standard Model with tau lepton signatures are also discussed.

Tau Physics with First Data in ATLAS

Tau leptons, and particularly their hadronic decays, will play an important role at the LHC. The goals for early tau physics in ATLAS include collecting a sample of tau leptons from data with a purity as high as possible so that the identification efficiency of hadronically decaying tau leptons can be measured and the simulation can be tuned. Collecting data with an integrated luminosity of a few hundred pb −1 at an instantaneous luminosity of 10 31 cm −2 s −1 will provide a unique opportunity in ATLAS to access and understand statistically significant tau samples from Standard Model processes at relatively low transverse momenta. Identification of tau leptons in ATLAS is particularly challenging due to overwhelming background coming from QCD dijet production. Processes like the production of W and Z bosons and top quark pairs will lead to samples of a few hundred to a few thousand identified hadronic tau decays. Hadronically decaying tau leptons will then become a well understood probe for discovery physics like searches for Higgs bosons, SUSY, or exotic phenomena. Feasibility studies for analyses which can be envisaged with an integrated luminosity of 100 pb −1 to 1 fb −1 are presented.

The use of maximum entropy in electromagnetic calorimeter event reconstruction

The method of Maximum Entropy has been applied to the reconstruction of particle positions and energy depositions in the electromagnetic calorimeter of the OPAL detector. The resulting information has been used in the identification of tau lepton decays into pions and ϱ mesons. The selection efficiencies and purities compare favourably with those obtained using the standard OPAL reconstruction algorithm.