Using gradient boosting and neural networks on simulated jets, the authors find lower mistagging rates than a cut-based tagger for hadronic four-top final states, at similar real efficiency.
k-NN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction
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Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes
Using gradient boosting and neural networks on simulated jets, the authors find lower mistagging rates than a cut-based tagger for hadronic four-top final states, at similar real efficiency.