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Full treatment of the thrust distribution in single inclusive e^+ e^- to h \, X processes

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arxiv 2306.02937 v1 pith:RTQOUSFZ submitted 2023-06-05 hep-ph

Full treatment of the thrust distribution in single inclusive e^+ e^- to h \, X processes

classification hep-ph
keywords thrustdependentmomentumtransversecorrelationsextendingrapiditytreatment
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Extending the transverse momentum dependent factorization to thrust dependent observables entails a series of difficulties, ultimately associated to the behavior of soft radiation. As a consequence, the definition of the transverse momentum dependent functions has to be revised, while preserving (and possibly extending) their universality properties. Moreover, the regularization of the rapidity divergences generates non trivial correlations between rapidity and thrust. In this paper, we show how to deal with these correlations in a consistent treatment of the thrust dependence of $e^+ e^- \to h \, X$ cross section, where the hadron transverse momentum is measured with respect to the thrust axis. In this framework all results obtained in the past few years properly fit together, leading to a remarkable phenomenological description of the experimental measurements.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Event-axis TMD measurements in $e^+e^-$ and SIDIS

    hep-ph 2026-07 unverdicted novelty 7.0

    Completes soft-operator formulation for thrust-axis TMD in e+e- and SIDIS, proposes nonperturbative model with event-shape dependence, resums logs, and validates against Pythia8.3 simulations.

  2. TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging

    hep-ph 2026-05 unverdicted novelty 7.0

    A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by observables.

  3. TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging

    hep-ph 2026-05 unverdicted novelty 7.0

    A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI sampling and SVD to extract parton distributions and identify unconstrained null components from multi-scale observables.