Tau lepton identification and reconstruction: a new frontier for jet-tagging ML algorithms
Torben Lange, Saswati Nandan, Joosep Pata, Laurits Tani, Christian, Veelken

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 decays () is an important task at current and future high-energy physics experiments, as 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 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 identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Particle Detector Development and Performance · Medical Imaging Techniques and Applications
