Improved Precision in $Vh(\rightarrow b\bar b)$ via Boosted Decision Trees

TL;DR

This paper explores the use of boosted decision trees to improve the precision of measuring $Vh( ightarrow b\bar b)$ production, resulting in modest but consistent improvements over traditional methods in setting bounds on SMEFT operators.

Contribution

It demonstrates the application of machine learning, specifically boosted decision trees, to enhance the analysis of $Vh$ production processes at hadron colliders.

Findings

Boosted decision trees provide a mild improvement in bounds on SMEFT operators.

Machine learning techniques can complement traditional cut-and-count analyses.

The approach is effective in leptonic decay channels of vector bosons.

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 $Vh(\rightarrow b\bar b)$ 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 $\mathcal{O}(\mathrm{few}\, \%)$ across the different operators.