Pith. sign in

REVIEW 3 cited by

Future tests of parton distributions

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 2103.08606 v1 pith:46Z2JMZO submitted 2021-03-15 hep-ph hep-ex

classification hep-phhep-ex
keywords datafuturetestwhethercurrentmethodologycompatibledataset
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We discuss a test of the generalization power of the methodology used in the determination of parton distribution functions (PDFs). The "future test" checks whether the uncertainty on PDFs, in regions in which they are not constrained by current data, are compatible with future data. The test is performed by using the current optimized methodology for PDF determination, but with a limited dataset, as available in the past, and by checking whether results are compatible within uncertainty with the result found using a current more extensive dataset. We use the future test to assess the generalization power of the NNPDF4.0 unpolarized PDF and the NNPDFpol1.1 polarized PDF methodology. Specifically, we investigate whether the former would predict the rise of the unpolarized proton structure function $F_2$ at small $x$ using only pre HERA data, and whether the latter would predict the so-called "proton spin crisis" using only pre-EMC data.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. Impact of Z-boson transverse-momentum resummation on PDF determination

    hep-ph 2026-07 conditional novelty 6.0 of 10

    N3LL' resummation is required to reconcile the 13 TeV ATLAS Z-pT spectrum with global PDF fits, but lowering the pT cut below 30 GeV is not yet supported.

  2. Quantitative Understanding of PDF Fits and their Uncertainties

    hep-ph 2025-12 conditional novelty 6.0 of 10

    After an initial transient, a PDF-fitting neural network's output obeys f_t = U(t) f_0 + V(t) Y, a linear blend of the initial network and the data with explicit time-dependent operators.

  3. Parton distributions confront LHC Run II data: a quantitative appraisal

    hep-ph 2025-01 conditional novelty 6.0 of 10

    A systematic NNLO data-theory comparison shows that the main PDF sets generalise to unseen LHC and HERA data about equally well once PDF, alpha_s, and missing higher order uncertainties are included.

Pith tools