Pith. sign in

REVIEW 6 cited by

Extraction of Pion Transverse Momentum Distributions from Drell-Yan data

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.01733 v1 pith:AQ45K34O submitted 2022-10-04 hep-ph

Extraction of Pion Transverse Momentum Distributions from Drell-Yan data

classification hep-ph
keywords momentumpiontmdstransverseunpolarizeddatacrossdistributions
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We map the distribution of unpolarized quarks inside a unpolarized pion as a function of the quark's transverse momentum, encoded in unpolarized Transverse Momentum Distributions (TMDs). We extract the pion TMDs from available data of unpolarized pion-nucleus Drell-Yan processes, where the cross section is differential in the lepton-pair transverse momentum. In the cross section, pion TMDs are convoluted with nucleon TMDs that we consistently take from our previous studies. We obtain a fairly good agreement with data. We present also predictions for pion-nucleus scattering that is being measured by the COMPASS Collaboration.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 6 Pith papers

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

  1. A first extraction of gluon TMDs from Higgs data at the LHC

    hep-ph 2026-05 unverdicted novelty 8.0

    First extraction of gluon TMDs from ATLAS and CMS Higgs q_T distributions at 8 and 13 TeV within TMD factorisation at N3LL accuracy.

  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.

  4. Simplified approach to extracting nucleon transversity in collinear factorization using near-side energy-energy correlators

    hep-ph 2026-04 unverdicted novelty 7.0

    A new method extracts the nucleon transversity PDF via near-side energy-energy correlators in dihadron fragmentation under collinear factorization, with leading-order results for SIDIS and e+e- annihilation that resem...

  5. Simplified approach to extracting nucleon transversity in collinear factorization using near-side energy-energy correlators

    hep-ph 2026-04 unverdicted novelty 7.0

    A new approach using near-side energy-energy correlators in dihadron fragmentation enables extraction of nucleon transversity PDF in collinear factorization without modeling intrinsic transverse momentum or dihadron r...

  6. Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data

    hep-ph 2026-07 conditional novelty 6.0

    Symbolic regression on a factorized NN fit to 482 Drell–Yan points yields a 9-constant analytical non-perturbative TMD with χ²/ndf≈1.04 and a retained x–b_T cross term.