Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees
Abstract
Extracting bounds on BSM operators at hadron colliders can be a highly non-trivial task. It can be useful or, depending on the complexity of the event structure, even essential to employ modern analysis techniques in order to measure New-Physics effects. A particular class of such modern methods are Machine-Learning algorithms, which are becoming more and more popular in particle physics. We attempt to gauge their potential in the study of production processes, focusing on the leptonic decay channels of the vector bosons. Specifically, we employ boosted decision trees using the kinematical information of a given event to discriminate between signal and background. Based on this analysis strategy, we derive bounds on four dimension-6 SMEFT operators and subsequently compare them with the ones obtained from a conventional cut-and-count analysis. We find a mild improvement of across the different operators.
Keywords
Cite
@article{arxiv.2407.21239,
title = {Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees},
author = {Philipp Englert},
journal= {arXiv preprint arXiv:2407.21239},
year = {2024}
}