<p>Using invariant-mass threshold resummation, we compute the invariant-mass distribution and the total cross section for <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13130_2025_27501_Article_IEq1.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="29" /> </InlineMediaObject> <EquationSource Format="MATHML"><math display="inline"> <mi>t</mi> <mover accent="true"> <mi>t</mi> <mo stretchy="true">¯</mo> </mover> <mi>t</mi> <mover accent="true"> <mi>t</mi> <mo stretchy="true">¯</mo> </mover> </math></EquationSource> <EquationSource Format="TEX">\( t\overline{t}t\overline{t} \)</EquationSource> </InlineEquation> production at the LHC with centre-of-mass energy of 13<i>.</i>6 TeV at NLL<sup>′</sup> accuracy. This accuracy includes next-to-leading logarithmic contributions together with relative 𝒪(<i>α</i><sub><i>s</i></sub>) non-logarithmic terms present in the threshold limit. We match the NLL<sup>′</sup> results to NLO in QCD and to complete NLO with electroweak corrections. We find that the inclusion of NLL<sup>′</sup> soft-gluon corrections significantly reduces the size of the theoretical uncertainties and greatly improves the convergence of the predictions when considering various choices of renormalisation and factorisation scales.</p>

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Invariant-mass threshold resummation for the production of four top quarks at the LHC

  • Melissa van Beekveld,
  • Anna Kulesza,
  • Michele Lupattelli,
  • Tommaso Saracco

摘要

Using invariant-mass threshold resummation, we compute the invariant-mass distribution and the total cross section for t t ¯ t t ¯ \( t\overline{t}t\overline{t} \) production at the LHC with centre-of-mass energy of 13.6 TeV at NLL accuracy. This accuracy includes next-to-leading logarithmic contributions together with relative 𝒪(αs) non-logarithmic terms present in the threshold limit. We match the NLL results to NLO in QCD and to complete NLO with electroweak corrections. We find that the inclusion of NLL soft-gluon corrections significantly reduces the size of the theoretical uncertainties and greatly improves the convergence of the predictions when considering various choices of renormalisation and factorisation scales.