arXiv subjects

Rabah Abdul Khalek

Publications and source records attributed to Rabah Abdul Khalek.

15 recordsLinked to original sources

arXiv2022

Pion and kaon fragmentation functions at next-to-next-to-leading order

We present a new determination of unpolarised charged pion and kaon fragmentation functions from a set of single-inclusive electron-positron annihilation and lepton-nucleon semi-inclusive deep-inelastic scattering data. The determination includes next-to-next-to-leading order QCD corrections to both processes, and is carried out in a framework that combines a neural-network parametrisation of fragmentation functions with a Monte Carlo representation of their uncertainties. We discuss the quality of the determination, in particular its dependence on higher order corrections.

hep-ph↗

arXiv2022

NNNPDF3.0: Evidence for a modified partonic structure in heavy nuclei

We present an updated determination of nuclear parton distributions (nPDFs) from a global NLO QCD analysis of hard processes in fixed-target lepton-nucleus and proton-nucleus together with collider proton-nucleus experiments. In addition to neutral- and charged-current deep-inelastic and Drell-Yan measurements on nuclear targets, we consider the information provided by the production of electroweak gauge bosons, isolated photons, jet pairs, and charmed mesons in proton-lead collisions at the LHC across centre-of-mass energies of 5.02 TeV (Run I) and 8.16 TeV (Run II). For the first time in a global nPDF analysis, the constraints from these various processes are accounted for both in the nuclear PDFs and in the free-proton PDF baseline. The extensive dataset underlying the nNNPDF3.0 determination, combined with its model-independent parametrisation, reveals strong evidence for nuclear-induced modifications of the partonic structure of heavy nuclei, specifically for the small-$x$ shadowing of gluons and sea quarks, as well as the large-$x$ anti-shadowing of gluons. As a representative phenomenological application, we provide predictions for ultra-high-energy neutrino-nucleon cross-sections, relevant for data interpretation at neutrino observatories. Our results provide key input for ongoing and future experimental programs, from that of heavy-ion collisions in controlled collider environments to the study of high-energy astrophysical processes.

hep-ph↗

arXiv2022

Impact of LHC dijet production in pp and pPb collisions on the nNNPDF2.0 nuclear PDFs

We quantify the impact of LHC dijet production measurements on the nNNPDF2.0 nuclear PDFs in twofold. First, from a proton baseline based on NNPDF3.1 and augmented by pp dijet production measurements from ATLAS and CMS at 7 and 8 TeV. Second, from a new nNNPDF2.0 global analysis including the ratio of pPb to pp dijet spectra from CMS at 5 TeV. We show that as opposed to the CMS at 5 TeV absolute pp and pPb dijet spectra, the pPb/pp ratio is well described in a nPDFs fit and provides strong constraints on the gluon of lead.

hep-ph↗

arXiv2021

Exploring the substructure of nucleons and nuclei with machine learning

Perturbative quantum chromodynamics (QCD) ceases to be applicable at low interaction energies due to the rapid increase of the strong coupling. In that limit, the non-perturbative regime determines the properties of quarks and gluons (partons) in terms of parton distribution functions (PDFs) or nuclear PDFs, based on whether they are confined within nucleons or nuclei respectively. Related non-perturbative dynamics describe the hadronisation of partons into hadrons and are encoded by the fragmentation functions (FFs). This thesis focuses on the detailed study of PDFs in protons and nuclei as well as the charged pions FFs by means of a statistical framework based on machine learning algorithms. The key ingredients are the Monte Carlo method for error propagation as well as artificial neural networks that act as universal unbiased interpolators. The main topics addressed are the inference of proton PDFs with theoretical uncertainties and the impact on the gluon PDF from dijet cross-sections; a global determination of nuclear PDFs exploiting the constraints from proton-lead collisions at the LHC and using for the first time NNLO calculations; a new determination of FFs from single-inclusive annihilation and semi-inclusive deep-inelastic scattering data; and a quantitative assessment of the impact of future colliders such as the High-Luminosity LHC and the Electron-Ion Collider on the proton and nuclear PDFs.

hep-ph↗

arXiv2021

A determination of unpolarised pion fragmentation functions using semi-inclusive deep-inelastic-scattering data: MAPFF1.0

We present MAPFF1.0, a determination of unpolarised charged-pion fragmentation functions (FFs) from a set of single-inclusive $e^+e^-$ annihilation and lepton-nucleon semi-inclusive deep-inelastic-scattering (SIDIS) data. FFs are parametrised in terms of a neural network (NN) and fitted to data exploiting the knowledge of the analytic derivative of the NN itself w.r.t. its free parameters. Uncertainties on the FFs are determined by means of the Monte Carlo sampling method properly accounting for all sources of experimental uncertainties, including that of parton distribution functions. Theoretical predictions for the relevant observables, as well as evolution effects, are computed to next-to-leading order (NLO) accuracy in perturbative QCD. We exploit the flavour sensitivity of the SIDIS measurements delivered by the HERMES and COMPASS experiments to determine a minimally-biased set of seven independent FF combinations. Moreover, we discuss the quality of the fit to the SIDIS data with low virtuality $Q^2$ showing that, as expected, low-$Q^2$ SIDIS measurements are generally harder to describe within a NLO-accurate perturbative framework.

hep-ph↗

arXiv2021

Self-consistent determination of proton and nuclear PDFs at the Electron Ion Collider

We quantify the impact of unpolarized lepton-proton and lepton-nucleus inclusive deep-inelastic scattering (DIS) cross section measurements from the future Electron-Ion Collider (EIC) on the proton and nuclear parton distribution functions (PDFs). To this purpose we include neutral- and charged-current DIS pseudodata in a self-consistent set of proton and nuclear global PDF determinations based on the NNPDF methodology. We demonstrate that the EIC measurements will reduce the uncertainty of the light quark PDFs of the proton at large values of the momentum fraction $x$, and, more significantly, of the quark and gluon PDFs of heavy nuclei, especially at small and large $x$. We illustrate the implications of the improved precision of nuclear PDFs for the interaction of ultra-high energy cosmic neutrinos with matter.

hep-ph↗

arXiv2020

Phenomenology of NNLO jet production at the LHC and its impact on parton distributions

We present a systematic investigation of jet production at hadron colliders from a phenomenological point of view, with the dual aim of providing a validation of theoretical calculations and guidance to future determinations of parton distributions (PDFs). We account for all available inclusive jet and dijet production measurements from ATLAS and CMS at 7 and 8 TeV by including them in a global PDF determination, and comparing to theoretical predictions at NNLO QCD supplemented by electroweak (EW) corrections. We assess the compatibility of the PDFs, specifically the gluon, obtained before and after inclusion of the jet data. We compare the single-inclusive jet and dijet observables in terms of perturbative behaviour upon inclusion of QCD and EW corrections, impact on the PDFs, and global fit quality. In the single-inclusive case, we also investigate the role played by different scale choices and the stability of the results upon changes in modelling of the correlated experimental systematics.

hep-ph↗

arXiv2020

nNNPDF2.0: Quark Flavor Separation in Nuclei from LHC Data

We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure functions used in our previous analysis, nNNPDF1.0, are complemented by inclusive and charm-tagged cross-sections from charged-current scattering. Furthermore, we include all available measurements of W and Z leptonic rapidity distributions in proton-lead collisions from ATLAS and CMS at $\sqrt{s}=5.02$ TeV and 8.16 TeV. The resulting nPDF determination, nNNPDF2.0, achieves a good description of all datasets. In addition to quantifying the nuclear modifications affecting individual quarks and antiquarks, we examine the implications for strangeness, assess the role that the momentum and valence sum rules play in nPDF extractions, and present predictions for representative phenomenological applications. Our results, made available via the LHAPDF library, highlight the potential of high-energy collider measurements to probe nuclear dynamics in a robust manner.

hep-ph↗

arXiv2020

On the derivatives of feed-forward neural networks

In this paper we present a C++ implementation of the analytic derivative of a feed-forward neural network with respect to its free parameters for an arbitrary architecture, known as back-propagation. We dubbed this code NNAD (Neural Network Analytic Derivatives) and interfaced it with the widely-used ceres-solver minimiser to fit neural networks to pseudodata in two different least-squares problems. The first is a direct fit of Legendre polynomials. The second is a somewhat more involved minimisation problem where the function to be fitted takes part in an integral. Finally, using a consistent framework, we assess the efficiency of our analytic derivative formula as compared to numerical and automatic differentiation as provided by ceres-solver. We thus demonstrate the advantage of using NNAD in problems involving both deep or shallow neural networks.

physics.comp-ph↗

arXiv2019

A First Determination of Parton Distributions with Theoretical Uncertainties

The parton distribution functions (PDFs) which characterize the structure of the proton are currently one of the dominant sources of uncertainty in the predictions for most processes measured at the Large Hadron Collider (LHC). Here we present the first extraction of the proton PDFs that accounts for the missing higher order uncertainty (MHOU) in the fixed-order QCD calculations used in PDF determinations. We demonstrate that the MHOU can be included as a contribution to the covariance matrix used for the PDF fit, and then introduce prescriptions for the computation of this covariance matrix using scale variations. We validate our results at next-to-leading order (NLO) by comparison to the known next order (NNLO) corrections. We then construct variants of the NNPDF3.1 NLO PDF set that include the effect of the MHOU, and assess their impact on the central values and uncertainties of the resulting PDFs.

hep-ph↗

arXiv2019

Parton Distributions with Theory Uncertainties: General Formalism and First Phenomenological Studies

We formulate a general approach to the inclusion of theoretical uncertainties, specifically those related to the missing higher order uncertainty (MHOU), in the determination of parton distribution functions (PDFs). We demonstrate how, under quite generic assumptions, theory uncertainties can be included as an extra contribution to the covariance matrix when determining PDFs from data. We then review, clarify, and systematize the use of renormalization and factorization scale variations as a means to estimate MHOUs consistently in deep inelastic and hadronic processes. We define a set of prescriptions for constructing a theory covariance matrix using scale variations, which can be used in global fits of data from a wide range of different processes, based on choosing a set of independent scale variations suitably correlated within and across processes. We set up an algebraic framework for the choice and validation of an optimal prescription by comparing the estimate of MHOU encoded in the next-to-leading order (NLO) theory covariance matrix to the observed shifts between NLO and NNLO predictions. We perform a NLO PDF determination which includes the MHOU, assess the impact of the inclusion of MHOUs on the PDF central values and uncertainties, and validate the results by comparison to the known shift between NLO and NNLO PDFs. We finally study the impact of the inclusion of MHOUs in a global PDF determination on LHC cross-sections, and provide guidelines for their use in precision phenomenology. In addition, we also compare the results based on the theory covariance matrix formalism to those obtained by performing PDF determinations based on different scale choices.

hep-ph↗

arXiv2019

Probing Proton Structure at the Large Hadron electron Collider

For the foreseeable future, the exploration of the high-energy frontier will be the domain of the Large Hadron Collider (LHC). Of particular significance will be its high-luminosity upgrade (HL-LHC), which will operate until the mid-2030s. In this endeavour, for the full exploitation of the HL-LHC physics potential an improved understanding of the parton distribution functions (PDFs) of the proton is critical. The HL-LHC program would be uniquely complemented by the proposed Large Hadron electron Collider (LHeC), a high-energy lepton-proton and lepton-nucleus collider based at CERN. In this work, we build on our recent PDF projections for the HL-LHC to assess the constraining power of the LHeC measurements of inclusive and heavy quark structure functions. We find that the impact of the LHeC would be significant, reducing PDF uncertainties by up to an order of magnitude in comparison to state-of-the-art global fits. In comparison to the HL-LHC projections, the PDF constraints from the LHeC are in general more significant for small and intermediate values of the momentum fraction x. At higher values of x, the impact of the LHeC and HL-LHC data is expected to be of a comparable size, with the HL-LHC constraints being more competitive in some cases, and the LHeC ones in others. Our results illustrate the encouraging complementarity of the HL-LHC and the LHeC in terms of charting the quark and gluon structure of the proton.

hep-ph↗

arXiv2019

Nuclear Parton Distributions from Lepton-Nucleus Scattering and the Impact of an Electron-Ion Collider

We present a first determination of the nuclear parton distribution functions (nPDF) based on the NNPDF methodology: nNNPDF1.0. This analysis is based on neutral-current deep-inelastic structure function data and is performed up to NNLO in QCD calculations with heavy quark mass effects. For the first time in the NNPDF fits, the $χ^2$ minimization is achieved using stochastic gradient descent with reverse-mode automatic differentiation (backpropagation). We validate the robustness of the fitting methodology through closure tests, assess the perturbative stability of the resulting nPDFs, and compare them with other recent analyses. The nNNPDF1.0 distributions satisfy the boundary condition whereby the NNPDF3.1 proton PDF central values and uncertainties are reproduced at $A=1$, which introduces important constraints particularly for low-$A$ nuclei. We also investigate the information that would be provided by an Electron-Ion Collider (EIC), finding that EIC measurements would significantly constrain the nPDFs down to $x\simeq 5\times 10^{-4}$. Our results represent the first-ever nPDF determination obtained using a Monte Carlo methodology consistent with that of state-of-the-art proton PDF fits, and provide the foundation for a subsequent global nPDF analyses including also proton-nucleus data.

hep-ph↗

arXiv2018

Nuclear Parton Distributions from Neural Networks

In this contribution we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account for the dependence on the atomic mass number $A$, and introduce novel algorithms to improve the training of the neural network parameters within the NNPDF framework. Finally, we present preliminary results of a nPDF fit to neutral current deep-inelastic lepton-nucleus scattering data, and demonstrate how one can validate the new fitting methodology by means of closure tests.

hep-ph↗

arXiv2018

Towards Ultimate Parton Distributions at the High-Luminosity LHC

Since its start of data taking, the LHC has provided an impressive wealth of information on the quark and gluon structure of the proton. Indeed, modern global analyses of parton distribution functions (PDFs) include a wide range of LHC measurements of processes such as the production of jets, electroweak gauge bosons, and top quark pairs. In this work, we assess the ultimate constraining power of LHC data on the PDFs that can be expected from the complete dataset, in particular after the High-Luminosity (HL) phase, starting in around 2025. The huge statistics of the HL-LHC, delivering $\mathcal{L}=3$ ab$^{-1}$ to ATLAS and CMS and $\mathcal{L}=0.3$ ab$^{-1}$ to LHCb, will lead to an extension of the kinematic coverage of PDF-sensitive measurements as well as to an improvement in their statistical and systematic uncertainties. Here we generate HL-LHC pseudo-data for different projections of the experimental uncertainties, and then quantify the resulting constraints on the PDF4LHC15 set by means of the Hessian profiling method. We find that HL-LHC measurements can reduce PDF uncertainties by up to a factor of 2 to 4 in comparison to state-of-the-art fits, leading to few-percent uncertainties for important observables such as the Higgs boson transverse momentum distribution via gluon-fusion. Our results illustrate the significant improvement in the precision of PDF fits achievable from hadron collider data alone, and motivate the continuation of the ongoing successful program of PDF-sensitive measurements by the LHC collaborations.

hep-ph↗