The thesis projects 5-sigma discovery reaches at the HL-LHC for charged Higgs pairs, Flavon decays, and h->eµ, using BDT-based event selection in the 2HDM-III and Froggatt-Nielsen models.
Boosted decision trees
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abstract
Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then mitigated with ensemble learning, using boosting algorithms, in particular AdaBoost and gradient boosting. Examples from high-energy physics and software used are also presented.
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Phenomenology of scalar particles assisted by machine learning
The thesis projects 5-sigma discovery reaches at the HL-LHC for charged Higgs pairs, Flavon decays, and h->eµ, using BDT-based event selection in the 2HDM-III and Froggatt-Nielsen models.