Particle-flow based tau identification at future e+e− colliders
Torben Lange et al.
Different algorithms for identifying hadronic τ decays (τh) at future high-energy e+e− colliders are studied and compared in terms of their performance. The algorithms studied include the "hadrons-plus-strips" and DeepTau algorithms developed by the CMS collaboration for pp collisions at the LHC and two novel neural network based algorithms, LorentzNet and ParticleTransformer, which have originally been developed for jet flavor tagging and are repurposed for the task of τh identification. The algorithms use particles reconstructed by the particle-flow method as input. The best performing algorithm achieves an average misidentification rate for quark and gluon jets of 4.0⋅10−5 for an average hadronic τ identification efficiency of 60%. This performance provides very promising prospects for physics analyses involving hadronic τ decays at future high-energy e+e− colliders.