LHC Study of Third-Generation Scalar Leptoquarks with Machine-Learned Likelihoods

TL;DR

This paper explores how machine learning enhances LHC searches for third-generation scalar leptoquarks, improving exclusion limits and projecting future sensitivities with unbinned methods and systematic uncertainty considerations.

Contribution

It introduces a novel unbinned machine learning approach for leptoquark searches, incorporating systematic uncertainties and projecting future collider sensitivities.

Findings

Exclusion limits reach up to ~1.3 TeV for certain branching fractions.

Projected sensitivities extend to ~1.6 TeV at 14 TeV with 300 fb$^{-1}$.

Systematic uncertainties are preliminarily included, assessing their impact on results.

Abstract

We study the impact of machine-learning algorithms on LHC searches for leptoquarks in final states with hadronically decaying tau leptons, multiple $b$-jets, and large missing transverse momentum. Pair production of scalar leptoquarks with decays only into third-generation leptons and quarks is assumed. Thanks to the use of supervised learning tools with unbinned methods to handle the high-dimensional final states, we consider simple selection cuts which would possibly translate into an improvement in the exclusion limits at the 95$\%$ confidence level for leptoquark masses with different values of their branching fraction into charged leptons. In particular, for intermediate branching fractions, we expect that the exclusion limits for leptoquark masses extend to $\sim$1.3 TeV. As a novelty in the implemented unbinned analysis, we include a simplified estimation of some systematic uncertainties with the aim of studying their possible impact on the stability of the results. Finally, we also present the projected sensitivity within this framework at 14 TeV for 300 and 3000 fb$^{-1}$ that extends the upper limits to $\sim$1.6 and $\sim$1.8 TeV, respectively.