A unified machine learning approach for reconstructing hadronically decaying tau leptons

TL;DR

This paper introduces a multi-task machine learning framework for reconstructing hadronically decaying tau leptons, achieving high precision in momentum and decay mode classification, and presents a new dataset for further research.

Contribution

It proposes a unified ML approach for tau lepton reconstruction, decomposing the task into sub-tasks and demonstrating state-of-the-art performance with a new publicly available dataset.

Findings

Achieved 2-3% momentum resolution across models

Decay mode classification accuracy between 80-95%

ParticleTransformer outperforms heuristic baselines

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 sub-tasks. 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%. We find ParticleTransformer to be the best-performing approach, significantly outperforming the heuristic baseline. This paper also serves as an introduction to a new publicly available $\mathtt{Fu}\tau\mathtt{ure}$ dataset for the development of tau reconstruction algorithms. This allows to further study the resilience of ML models to domain shifts and the efficient use of foundation models for such tasks.