Tau Lepton Identification With Graph Neural Networks at Future Electron–Positron Colliders - Giagu, Stefano; Torresi , Luca; Di Filippo, Matteo (Frontiers in Physics, 2022)
Efficient and accurate reconstruction and identification of tau lepton decays plays a crucial role in the program of measurements and searches under study for the future high-energy particle colliders. Leveraging recent advances in Machine Learning algorithms, that have dramatically improved the state-of-the-art in visual object recognition, we have developed novel tau identification methods 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.