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<p>We investigate the collinear matching of transverse momentum dependent (TMD) distributions at large values of <i>x</i>, computing and resumming the leading large-<i>x</i> asymptotics for matching coefficients. The large-<i>x</i> resummation is done directly within TMD distributions, ensuring the process-independence of the result. The derived resummation formulas are valid for all TMD distributions (except the pretzelosity). Their application improves perturbative convergence, provides practical estimation for unknown higher-order contributions, and sets restrictions for the nonperturbative part of models. Using the known anomalous dimensions, resummation can reach N<sup>3</sup>LL, often exceeding the accuracy of known coefficient functions.</p>

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Transverse momentum distributions at large-x

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月24日

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  • Oscar del Rio,
  • Alexei Prokudin,
  • Ignazio Scimemi,
  • Alexey Vladimirov

摘要

We investigate the collinear matching of transverse momentum dependent (TMD) distributions at large values of x, computing and resumming the leading large-x asymptotics for matching coefficients. The large-x resummation is done directly within TMD distributions, ensuring the process-independence of the result. The derived resummation formulas are valid for all TMD distributions (except the pretzelosity). Their application improves perturbative convergence, provides practical estimation for unknown higher-order contributions, and sets restrictions for the nonperturbative part of models. Using the known anomalous dimensions, resummation can reach N3LL, often exceeding the accuracy of known coefficient functions.