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arXiv · 2312.08276

Neural networks for boosted di-$\tau$ identification

Nadav Tamir·Ilan Bessudo·Boping Chen·Hely Raiko·Liron Barak
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Abstract

We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-$\tau$ topologies against background QCD jets, using calorimeter and tracking information. Boosted di-$\tau$ topologies consisting of a pair of highly collimated $\tau$-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.

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Nadav Tamir, Ilan Bessudo, Boping Chen, Hely Raiko, Liron Barak. 2023-12-13. Neural networks for boosted di-$\tau$ identification. https://doi.org/10.1088/1748-0221/19/07/p07004

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