Determination of diffractive PDFs from global QCD analysis of inclusive diffractive DIS and dijet cross-section measurements at HERA
arXiv:2301.10284 · doi:10.1103/PhysRevD.107.094038
Abstract
We present an updated set of {\tt SKMHS} diffractive parton distribution functions (PDFs). In addition to the diffractive deep-inelastic scattering (diffractive DIS) data sets, the recent diffractive dijet cross sections measurements by the H1 experiment from the HERA collider are added to the data sample. The new set of diffractive PDFs, entitled {\tt SKMHS23} and {\tt SKMHS23-dijet}, are presented at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) accuracy in perturbative QCD. Since the gluons directly contribute to jet production through the boson-gluon fusion process, the data on diffractive dijet production in inclusive DIS help to constrain the gluon density, allowing for the determination of both the quark and gluon densities with better accuracy. The NLO and NNLO theory predictions calculated using both {\tt SKMHS23} and {\tt SKMHS23-dijet} are compared to the analyzed data showing excellent agreements. The effect arising from the inclusion of diffractive dijet data and higher order QCD corrections on the extracted diffractive PDFs and data/theory agreements are clearly examined and discussed.
arXiv comment19 pages, 11 figures and 5 tables
References in corpus (29)
- Parton distributions for the LHC Run II
- LHAPDF6: parton density access in the LHC precision era
- Implications of CTEQ global analysis for collider observables
- The p_T Spectrum in Heavy-Flavour Hadroproduction
- APFEL: A PDF Evolution Library with QED corrections
- Heavy quarks in deep-inelastic scattering
- Proof of Factorization for Diffractive Hard Scattering
- Measurement and QCD Analysis of the Diffractive Deep-Inelastic Scattering Cross Section at HERA
- The Large Hadron-Electron Collider at the HL-LHC
- Dijet Cross Sections and Parton Densities in Diffractive DIS at HERA
- Multi-jet cross sections in deep inelastic scattering at next-to-leading order
- Higgs production in bottom-quark fusion in a matched scheme
- NNLO QCD corrections to jet production in deep inelastic scattering
- Tests of QCD Factorisation in the Diffractive Production of Dijets in Deep-Inelastic Scattering and Photoproduction at HERA
- A QCD analysis of ZEUS diffractive data
- Precise QCD predictions for the production of dijet final states in deep inelastic scattering
- Deep inelastic scattering with leading protons or large rapidity gaps at HERA
- Measurement of the Diffractive Deep-Inelastic Scattering Cross Section with a Leading Proton at HERA
- Diffractive Open Charm Production in Deep-Inelastic Scattering and Photoproduction at HERA
- Combined inclusive diffractive cross sections measured with forward proton spectrometers in deep inelastic ep scattering at HERA
- Inclusive Measurement of Diffractive Deep-Inelastic Scattering at HERA
- Measurement of Dijet Production in Diffractive Deep-Inelastic Scattering with a Leading Proton at HERA
- The Tensor Pomeron and Low-x Deep Inelastic Scattering
- Selected topics in diffraction with protons and nuclei: past, present, and future
- First global next-to-leading order determination of diffractive parton distribution functions and their uncertainties within the {\tt xFitter} framework
- Phenomenology of diffractive DIS in the framework of fracture functions and determination of diffractive parton distribution functions
- Dijet production in diffractive deep-inelastic scattering in next-to-next-to-leading order QCD
- Measurement of the Diffractive Longitudinal Structure Function F_L^D at HERA
- Role of higher twist effects in diffractive DIS and determination of diffractive parton distribution functions
Cited by in corpus (4)
- Twist-3 Contributions in Semi-Inclusive DIS in the Target Fragmentation Region
- Gluonic contributions to semi-inclusive DIS in the target fragmentation region
- Effective Field Theory Factorization for Diffraction
- Impact of neutral fluxes and signal significance optimization on semi-exclusive production via deep learning training