Abstract
Abstract
We train several neural networks and boosted decision trees to discriminate fully-hadronic
boosted di-τ topologies against background QCD jets, using calorimeter and tracking
information. Boosted di-τ topologies consisting of a pair of highly collimated
τ-leptons, arise from the decay of a highly energetic Standard Model Higgs or Z boson or from
particles beyond the Standard Model. We compare the tagging performance for different
neural-network models and a boosted decision tree, the latter serving as a simple benchmark
machine learning model.
The code used to obtain the results presented in this paper is available on GitHub.