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Probing fragmentation functions from same-side hadron-jet momentum correlations in p-p collisions

Francois Arleo, Michel Fontannaz, Jean-Philippe Guillet, Chi Linh Nguyen

TL;DR

This study analyzes hadron–jet momentum correlations in pp collisions at the LHC at NLO using JETPHOX to test fragmentation-function (FF) sets. By examining the hadron momentum fraction z_h inside jets and comparing inclusive charged hadrons as well as identified kaons and protons across multiple FF parametrizations (AKK08, BFGW, DSS, HKNS, Kretzer), the authors show substantial FF-dependent differences that often exceed the inherent scale uncertainties. Normalized distributions are shown to reduce scale sensitivity and sharpen distinctions between FF sets, highlighting strong sensitivity to gluon fragmentation at large z. The results suggest that such jet-in-hadron observables can provide valuable additional constraints on FFs, especially for poorly constrained gluon FFs, with pronounced benefits when hadron identification is available up to large momenta.

Abstract

A next-to-leading order (NLO) analysis of hadron-jet momentum correlations in p-p collisions at the LHC is carried out. We show that the inclusive charged hadron momentum distributions inside jets is a very sensitive observable which allows one to disentangle among various fragmentation function sets presently available. Correlations with identified hadrons (kaons, protons) are investigated as well.

Probing fragmentation functions from same-side hadron-jet momentum correlations in p-p collisions

TL;DR

This study analyzes hadron–jet momentum correlations in pp collisions at the LHC at NLO using JETPHOX to test fragmentation-function (FF) sets. By examining the hadron momentum fraction z_h inside jets and comparing inclusive charged hadrons as well as identified kaons and protons across multiple FF parametrizations (AKK08, BFGW, DSS, HKNS, Kretzer), the authors show substantial FF-dependent differences that often exceed the inherent scale uncertainties. Normalized distributions are shown to reduce scale sensitivity and sharpen distinctions between FF sets, highlighting strong sensitivity to gluon fragmentation at large z. The results suggest that such jet-in-hadron observables can provide valuable additional constraints on FFs, especially for poorly constrained gluon FFs, with pronounced benefits when hadron identification is available up to large momenta.

Abstract

A next-to-leading order (NLO) analysis of hadron-jet momentum correlations in p-p collisions at the LHC is carried out. We show that the inclusive charged hadron momentum distributions inside jets is a very sensitive observable which allows one to disentangle among various fragmentation function sets presently available. Correlations with identified hadrons (kaons, protons) are investigated as well.

Paper Structure

This paper contains 13 sections, 1 equation, 6 figures, 1 table.

Figures (6)

  • Figure 1: Comparison of gluon fragmentation functions into charged hadrons (left) and protons+anti-protons (right), as a function of $z$ and at $Q^2=100$ GeV$^2$.
  • Figure 2: Relative contribution of gluon fragmentation to the production of charged hadrons (left) and protons+antiprotons (right) inside jets, using the various FF sets. See text for details.
  • Figure 3: Left: $z_{_{\rm h}}$ distributions of charged hadrons inside jets, using the AKK08, BFGW, DSS, HKNS and Kretzer FF sets. The band indicates the scale dependence of the DSS calculation (see text). Right: Same distributions normalized to the DSS prediction.
  • Figure 4: Left: Normalized $z_{_{\rm h}}$ distributions of charged hadrons inside jets, using the AKK08, BFGW, DSS, HKNS and Kretzer FF sets. The band indicates the scale dependence of the DSS calculation (see text). Right: Same distributions normalized to the DSS prediction.
  • Figure 5: Left: $z_{_{\rm h}}$ distributions of charged kaons inside jets, using the AKK08, DSS and Kretzer FF sets. The band indicates the scale dependence of the DSS calculation (see text). Right: Same distributions normalized to the DSS prediction.
  • ...and 1 more figures