• The tZq signal is observed with a significance well over five standard deviations.
• Machine Learning algorithm, gradient boosted decision trees (BDTs) are set up to maximally discriminate between prompt and non-prompt leptons.
• The BDTs exploit the properties of the jet closest to the lepton in terms of ∆R
• The measured tZq production cross section is in agreement with the standard model expectation.
• Python implementation of ROOT TMVA (cern machine learning and data analysis platform) used.

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