Abstract

We study the resummation of soft gluon emission corrections to the production of a top-antitop pair in association with a Higgs boson at the Large Hadron Collider. Starting from a soft-gluon resummation formula derived in previous work, we develop a bespoke parton-level Monte Carlo program which can be used to calculate the total cross section along with differential distributions. We use this tool to study the phenomenological impact of the resummation to next-to-next-to-leading logarithmic (NNLL) accuracy, finding that these corrections increase the total cross section and the differential distributions with respect to NLO calculations of the same observables.

Highlights

  • JHEP02(2017)126 will be computed in the near future

  • In this paper we evaluated the resummation of the soft emission corrections to the associated production of a top-quark pair and a Higgs boson at the LHC in the partonic threshold limit z → 1

  • The numerical evaluation of observables at NLO+next-to-next-to-leading logarithmic (NNLL) was carried out by means of an in-house parton level Monte Carlo code developed for this work, based on the resummation formula derived in [16]

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Summary

Outline of the calculation

The associated production of a top quark pair and a Higgs boson receives contributions from the partonic process i(p1) + j(p2) −→ t(p3) + t(p4) + H(p5) + X ,. The hard and soft functions in (2.8) can be evaluated in fixed order perturbation theory at scales at which they are free from large logarithms. We indicate these scales with μh and μs, respectively. One can see that the l.h.s. of (2.10) is independent of μh and μs if the evolution factors and the hard and soft functions are known to all orders in perturbation theory This is impossible in practice, which introduces a residual dependence on the choice of the scales μh and μs in any numerical evaluation of (2.11) or (2.12). These can be constructed using techniques described in [40, 41]

Numerical results
13 TeV and MMHT
Scale choices
Total cross section
Differential distributions
Findings
Conclusions

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