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The MUST (Mass Unspecific Supervised Tagging) method has proven to besuccessful in implementing generic jet taggers capable of discriminatingvarious signals over a wide range of jet masses. We implement the MUST conceptby using eXtreme Gradient Boosting ({\tt XGBoost}) classifiers instead ofneural networks (NNs) as previously done. We build both fully-generic andspecific multi-pronged taggers, to identify 2, 3, and/or 4-pronged signals fromSM QCD background. We show that {\tt XGBoost}-based taggers are not only easierto optimize and much faster than those based in NNs, but also show quitesimilar performance, even when testing with signals not used in training,providing an alternative machine-learning implementation that can potentiallybe used to test the robustness of the results.

Gradient Boosting MUST taggers for highly-boosted jets