Abstract

We assess and compare different methods for including leading threshold logarithms at next-to-leading-power in prompt photon production at hadron colliders, for both the direct and parton fragmentation mechanisms. We do this in addition to next-to-leading logarithmic threshold and joint resummation at leading power. We study the size of these effects and their scale variations for LHC kinematics. We find that the next-to-leading power effects have a noticeable effect on the photon transverse momentum distribution, typically of order $\mathcal{O}(10\%)$, depending on the method of inclusion. Our results indicate that next-to-leading power terms can reduce the scale dependence of the distribution considerably.

Highlights

  • We consider the production of a prompt photon with a given transverse momentum pT in hadronic collisions

  • We study the size of these effects and their scale variations for Large Hadron Collider kinematics

  • The parameter μin the second line of (14) acts as a cut off on the recoil transverse momentum to avoid the singularity in the kinematic factor at pT 1⁄4 QT=2, where the assumption that QT is small compared to pT is not valid

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Summary

INTRODUCTION

We consider the production of a prompt photon with a given transverse momentum pT in hadronic collisions. In [17,18] it was shown, to NLL accuracy, that threshold logarithms can be resummed jointly with recoil corrections This joint resummation has been applied to direct prompt photon production in [19], heavy quark production in [20], BSM processes in [21,22,23], and to vector boson and Higgs production in [24,25]. Preliminary studies [67,68,69] were performed for the resummation of a large class of leading logarithmic (LL with m 1⁄4 2n − 1) NLP terms for direct production of prompt photons, in both threshold and joint resummation. II, while in Appendix B we compare the NLO expansion of our resummed expressions at NLP with exact results

RESUMMATION
Threshold resummation
Joint resummation
Initial state threshold logarithms at next-to-leading power
Similarity between the two approaches at LP
Differences between methods at the next-to-leading power level
Final state next-to-leading power terms
NUMERICAL STUDIES
Initial state NLP terms
Final state NLP terms
Combined result
Findings
CONCLUSIONS
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