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.
Original language | English |
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Article number | P07004 |
Journal | Journal of Instrumentation |
Volume | 19 |
Issue number | 7 |
DOIs | |
State | Published - 1 Jul 2024 |
Keywords
- Analysis and statistical methods
- Particle identification methods
All Science Journal Classification (ASJC) codes
- Instrumentation
- Mathematical Physics