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.
@article{arxiv.2312.08276,
title = {Neural networks for boosted di-$\tau$ identification},
author = {Nadav Tamir and Ilan Bessudo and Boping Chen and Hely Raiko and Liron Barak},
journal= {arXiv preprint arXiv:2312.08276},
year = {2024}
}