We study the impact of machine-learning algorithms on LHC searches for leptoquarks in final states with hadronically decaying tau leptons, multiple <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi>b</a:mi></a:math>-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 <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mo>∼</c:mo><c:mn>1.3</c:mn><c:mtext> </c:mtext><c:mtext> </c:mtext><c:mi>TeV</c:mi></c:math>. 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 <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"><e:mn>300</e:mn><e:mtext> </e:mtext><e:mtext> </e:mtext><e:msup><e:mi>fb</e:mi><e:mrow><e:mo>−</e:mo><e:mn>1</e:mn></e:mrow></e:msup></e:math> and <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:mn>3000</g:mn><g:mtext> </g:mtext><g:mtext> </g:mtext><g:msup><g:mi>fb</g:mi><g:mrow><g:mo>−</g:mo><g:mn>1</g:mn></g:mrow></g:msup></g:math> that extends the upper limits to <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"><i:mo>∼</i:mo><i:mn>1.6</i:mn><i:mtext> </i:mtext><i:mtext> </i:mtext><i:mi>TeV</i:mi></i:math> and <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"><k:mo>∼</k:mo><k:mn>1.8</k:mn><k:mtext> </k:mtext><k:mtext> </k:mtext><k:mi>TeV</k:mi></k:math>, respectively.
No takes yet. Share an insight, caveat, or question.
Arganda et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: