Publication detailsCzakon, Michal, Ferroglia, Andrea, Heymes, David, Mitov, Alexander, Pecjak, Ben D., Scott, Darren J., Wang, Xing & Yang, Li Lin (2018). Resummation for (boosted) top-quark pair production at NNLO+NNLL′ in QCD. Journal of High Energy Physics 2018(5): 149.
- Publication type: Journal Article
- ISSN/ISBN: 1126-6708, 1029-8479
- DOI: 10.1007/JHEP05(2018)149
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
We construct predictions for top quark pair differential distributions at hadron colliders that combine state-of-the-art NNLO QCD calculations with double resummation at NNLL′ accuracy of threshold logarithms arising from soft gluon emissions and of small mass logarithms. This is the first time a resummed calculation at full NNLO+NNLL′ accuracy in QCD for a process with non-trivial color structure has been completed at the differential level. Of main interest to us is the stability of the Mtt¯ and top-quark p T distributions in the boosted regime where fixed order calculations may become strongly dependent on the choice of dynamic scales. With the help of numeric and analytic arguments we confirm that the choice for the factorization and renormalization scales advocated recently by some of the authors is indeed optimal. We further derive a set of optimized kinematics-dependent scales for the matching functions which appear in the resummed calculations. Our NNLO+NNLL′ prediction for the top-pair invariant mass is significantly less sensitive to the choice of factorization scale than the fixed order prediction, even at NNLO. Notably, the resummed and fixed order calculations are in nearly perfect agreement with each other in the full Mtt¯ range when the optimal dynamic scale is used. For the top-quark p T distribution the resummation performed here has less of an impact and instead we find that upgrading the matching with fixed-order from NLO+NNLL′ to NNLO+NNLL′ to be an important effect, a point to be kept in mind when using NLO-based Monte Carlo event generators to calculate this distribution.