A unified machine learning approach for reconstructing hadronically decaying tau leptons

Abstract

Tau leptons serve as an important tool for studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. Accurate reconstruction and identification of hadronically decaying tau leptons is a crucial task for current and future high energy physics experiments. Given the advances in jet tagging, we demonstrate how tau lepton reconstruction can be decomposed into tau identification, kinematic reconstruction, and decay mode classification in a multi-task machine learning setup.Based on an electron-positron collision dataset with full detector simulation and reconstruction, we show that common jet tagging architectures can be effectively used for these subtasks. We achieve comparable momentum resolutions of 2-3% with all the tested models, while the precision of reconstructing individual decay modes is between 80-95%. This paper also serves as an introduction to a new publicly available Fu{\tau}ure dataset and provides recipes for the development and training of tau reconstruction algorithms, while allowing to study resilience to domain shifts and the use of foundation models for such tasks.