2 citations · 2 across the 6 of their papers we have counts for
7 papers
Parton and nuclear dynamics at large : the CJ26 global QCD analysis
Alberto Accardi, Matteo Cerutti, Cynthia E. Keppel +4
We present the new CTEQ-JLab global QCD analysis, called CJ26, that simultaneously fits parton distribution functions of the proton and nuclear effects in deuteron targets. This wo…
Probing Dense Nuclear Matter at Small-x: A workflow for a global analysis framework
Junaid S. Khan, Rebecca L. Lustberg, Fredrick Olness +3
The dipole model provides a powerful framework for describing high-energy nuclear interactions, particularly in the regime of dense gluonic matter. However, accurately evolving the…
CJ26 Global QCD Analysis with Large- Jefferson Lab 6 and 12 GeV Data
Alberto Accardi, Matteo Cerutti, Cynthia E. Keppel +4
We present CJ26, the new CTEQ-JLab global QCD analysis that incorporates for the first time the complete suite of JLab 6 GeV DIS measurements and the first published JLab 12 GeV me…
Heavy-quark contributions to the DIS structure functions and at NLO in the ACOT scheme
Edoardo Spezzano, Tomas Jezo, Michael Klasen +2
We compute the contributions of heavy quarks to the deep-inelastic scattering structure functions and at next-to-leading order of perturbative QCD in the ACOT scheme. B…
Determination of proton PDF uncertainties with Markov chain Monte Carlo
Peter Risse, Nasim Derakhshanian, Tomas Jezo +2
We present an analysis of parton distribution functions (PDFs) of the proton using Markov Chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes' theorem an…
Heavy quark mass effects in charged-current deep-inelastic scattering at approximate NNLO in the Aivazis-Collins-Olness-Tung scheme
Peter Risse, Valerio Bertone, Tomas Ježo +4
The approximate SACOT- scheme for heavy quark production in deep-inelastic scattering was initially formulated for the neutral current structure functions and . We ex…
A Markov Chain Monte Carlo determination of Proton PDF uncertainties at NNLO
Peter Risse, Nasim Derakhshanian, Tomas Ježo +2
The current scientific standard in PDF uncertainty estimation relies either on repeated fits over artificially generated data to arrive at Monte Carlo samples of best fits or on th…