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NNLOCAL: Fully Local Subtractions for Precision Predictions in Hadron Collisions

Vittorio Del Duca, Claude Duhr, Levente Fekésházy, Flavio Guadagni, Pooja Mukherjee, Gábor Somogyi, Francesco Tramontano, Sam Van Thurenhout

Abstract

This work extends the CoLoRFulNNLO subtraction method to address soft and collinear divergences in the computation of higher-order corrections for hadronic collisions. By utilizing universal local counterterms which can be integrated analytically over the unresolved phase space, we achieve numerically stable, fully-differential predictions. Our publicly available NNLOCAL code serves as a proof-of-concept implementation, validated by calculating the NNLO cross-section for Higgs boson production in gluon-gluon fusion with no light quarks.

NNLOCAL: Fully Local Subtractions for Precision Predictions in Hadron Collisions

Abstract

This work extends the CoLoRFulNNLO subtraction method to address soft and collinear divergences in the computation of higher-order corrections for hadronic collisions. By utilizing universal local counterterms which can be integrated analytically over the unresolved phase space, we achieve numerically stable, fully-differential predictions. Our publicly available NNLOCAL code serves as a proof-of-concept implementation, validated by calculating the NNLO cross-section for Higgs boson production in gluon-gluon fusion with no light quarks.
Paper Structure (5 sections, 6 equations, 1 figure)

This paper contains 5 sections, 6 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Formalism of the Local Subtraction Method
  3. Computation overview
  4. Numerical Implementation
  5. Conclusion

Figures (1)

  • Figure 1: The rapidity distribution of the Higgs boson at NNLO in the HEFT with $n_f=0$ light quarks. The distribution on the left has a bin width $\Delta y=0.25$, while the one on the right has a bin width of $\Delta y=0.1$. The lower panels show the relative error for the total distribution and the NNLO contribution. The error bands shown are the estimated Monte Carlo uncertainties. Figures taken from DelDuca:2024ovc.