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

TL;DR

This paper demonstrates that deep learning algorithms originally designed for jet-flavour tagging, such as LorentzNet and ParticleTransformer, can be effectively adapted for tau lepton identification, outperforming existing methods.

Contribution

It introduces an end-to-end transformer-based approach for tau identification, extending jet-flavour tagging algorithms to improve tau reconstruction accuracy.

Findings

Transformer-based algorithms outperform current tau identification methods.

Deep learning models can be adapted from jet-flavour tagging to tau identification.

End-to-end models show significant performance improvements.

Abstract

Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $\tau_{\textrm{h}}$ 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 $\tau_{\textrm{h}}$ 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 $\tau_{\textrm{h}}$ 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 $\tau_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $\tau_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider.