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$ψ(2S)$ production in jets using NRQCD

Marston Copeland, Lin Dai, Yu Fu, Jyotirmoy Roy

TL;DR

The paper addresses constraining the $ψ(2S)$ long-distance matrix elements (LDMEs) in NRQCD by studying $ψ(2S)$ production inside jets. It combines NRQCD factorization with two jet-fragmentation formalisms, the Fragmenting Jet Function (FJF) and Gluon Fragmentation Improved Pythia (GFIP), to predict the jet-internal $ψ(2S)$ distribution and compares against LHCb data at $ ms{\sqrt{s}}=13$ TeV. The results show that both formalisms significantly improve over naive NRQCD+Pythia predictions, with GFIP generally aligning with data across LDME sets and FJF’s performance strongly dependent on the chosen LDME extraction, particularly near the endpoint $z\to1$. The study demonstrates that in-jet observables provide a powerful discriminator among LDME extractions and motivates global fits incorporating jet data to tighten constraints on the $ψ(2S)$ LDMEs, thus advancing our understanding of quarkonium production in QCD.

Abstract

Based on recent data from LHCb, we study $ψ(2S)$ production in jets using non-relativistic QCD (NRQCD) in conjunction with the Fragmenting Jet Function (FJF) and Gluon Fragmentation Improved Pythia (GFIP) formalisms. Similar to previous studies of $J/ψ$ production in jets, our results show that these formalisms offer a much better description of data than the default Pythia+NRQCD prediction. We compare and contrast the predictions from the FJF formalism and the GFIP approach. In addition, our results show that the distribution of $ψ(2S)$ in jets is an excellent discriminator to test different predictions for the $ψ(2S)$ LDMEs from various extractions. We find a large disparity between the predictions from three different collaborations, showing that a more precise extraction of the $ψ(2S)$ LDMEs may be necessary.

$ψ(2S)$ production in jets using NRQCD

TL;DR

The paper addresses constraining the long-distance matrix elements (LDMEs) in NRQCD by studying production inside jets. It combines NRQCD factorization with two jet-fragmentation formalisms, the Fragmenting Jet Function (FJF) and Gluon Fragmentation Improved Pythia (GFIP), to predict the jet-internal distribution and compares against LHCb data at TeV. The results show that both formalisms significantly improve over naive NRQCD+Pythia predictions, with GFIP generally aligning with data across LDME sets and FJF’s performance strongly dependent on the chosen LDME extraction, particularly near the endpoint . The study demonstrates that in-jet observables provide a powerful discriminator among LDME extractions and motivates global fits incorporating jet data to tighten constraints on the LDMEs, thus advancing our understanding of quarkonium production in QCD.

Abstract

Based on recent data from LHCb, we study production in jets using non-relativistic QCD (NRQCD) in conjunction with the Fragmenting Jet Function (FJF) and Gluon Fragmentation Improved Pythia (GFIP) formalisms. Similar to previous studies of production in jets, our results show that these formalisms offer a much better description of data than the default Pythia+NRQCD prediction. We compare and contrast the predictions from the FJF formalism and the GFIP approach. In addition, our results show that the distribution of in jets is an excellent discriminator to test different predictions for the LDMEs from various extractions. We find a large disparity between the predictions from three different collaborations, showing that a more precise extraction of the LDMEs may be necessary.

Paper Structure

This paper contains 8 sections, 31 equations, 2 figures, 1 table.

Figures (2)

  • Figure 1: Comparison of different theoretical treatments for quarkonium production inside jets.
  • Figure 2: $z$-distributions of $\psi(2S)$ inside jets. Black dots are measured at the LHCb experiment with collision energy $\sqrt{s}=13$ TeV. The red and blue curves represent the results from the GFIP and FJF methods, respectively. Each row presents the results at a certain $p_T^{\text{jet}}$-bin with three different groups of LDMEs.