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

Abstract

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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.

Keywords

• Bayesian inference
• graph neural networks
• calorimetry
• tau identification
• dual-readout
• IDEA calorimeter