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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 ofHiggs and electroweak bosons as well as to search for physics beyond theStandard Model. The identification of $\tau_{\textrm{h}}$ can be viewed as ageneralization and extension of jet-flavour tagging, which has in the recentyears undergone significant progress due to the use of deep learning. Based ona granular simulation with realistic detector effects and a particle flow-basedevent reconstruction, we show in this paper that deep learning-basedjet-flavour-tagging algorithms are powerful $\tau_{\textrm{h}}$ identifiers.Specifically, we show that jet-flavour-tagging algorithms such as LorentzNetand ParticleTransformer can be adapted in an end-to-end fashion fordiscriminating $\tau_{\textrm{h}}$ from quark and gluon jets. We find that theend-to-end transformer-based approach significantly outperforms contemporarystate-of-the-art $\tau_{\textrm{h}}$ reconstruction and identificationalgorithms currently in use at the Large Hadron Collider.

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