Pith. sign in

REVIEW 6 cited by

Reconstruction of Monte Carlo replicas from Hessian 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 1607.06066 v2 pith:4G3MXFSC submitted 2016-07-20 hep-ph

Reconstruction of Monte Carlo replicas from Hessian parton distributions

classification hep-ph
keywords hessianmonte-carloct14partonreplicasdistributionspdfsasymmetric
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We explore connections between two common methods for quantifying the uncertainty in parton distribution functions (PDFs), based on the Hessian error matrix and Monte-Carlo sampling. CT14 parton distributions in the Hessian representation are converted into Monte-Carlo replicas by a numerical method that reproduces important properties of CT14 Hessian PDFs: the asymmetry of CT14 uncertainties and positivity of individual parton distributions. The ensembles of CT14 Monte-Carlo replicas constructed this way at NNLO and NLO are suitable for various collider applications, such as cross section reweighting. Master formulas for computation of asymmetric standard deviations in the Monte-Carlo representation are derived. A correction is proposed to address a bias in asymmetric uncertainties introduced by the Taylor series approximation. A numerical program is made available for conversion of Hessian PDFs into Monte-Carlo replicas according to normal, log-normal, and Watt-Thorne sampling procedures.

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. 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...

  2. 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...

  3. AI-assisted modeling and Bayesian inference of unpolarized quark transverse momentum distributions from Drell-Yan data

    hep-ph 2026-04 unverdicted novelty 6.0

    An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.

  4. Transverse-spin dependent energy-energy correlators in proton-proton collisions within the dihadron fragmentation framework

    hep-ph 2026-07 unverdicted novelty 5.0

    Numerical predictions for transverse-spin dependent energy-energy correlators in polarized pp collisions agree with recent STAR data and show a slight preference for transversity extractions consistent with lattice QCD.

  5. New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC

    hep-ph 2019-12 unverdicted novelty 5.0

    New CT18 PDFs at NLO and NNLO from global fit to HERA plus LHC jet, Drell-Yan, top-pair and Z data, with Hessian errors, Lagrange-multiplier studies, and alternate sets for data tensions and scale choices.

  6. Probing SU(2) Quark Flavor Asymmetry with W Bosons at RHIC

    hep-ph 2026-06 unverdicted novelty 3.0

    The STAR W+/W- cross section ratio is minimally affected by fiducial constraints and pT resummation, providing robust discrimination between ar d(x) and ar u(x) PDF models at x ~ 0.1 via L2 sensitivity and reweighting.