Fragmentation Functions for $Ξ^-/\barΞ^+$ Using Neural Networks

Soleymaninia, Maryam; Hadi, Hashamipour,; Khanpour, Hamzeh; Spiesberger, H.

doi:10.48550/arxiv.2202.05586

Cited by 2 publications

(3 citation statements)

References 44 publications

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“…There are a few ways to minimize a χ 2 function in a QCD fit which is based on Neural Network; one that readily comes to mind is explicit differentiation and calculation of global minimum directly, this approach is inefficient and sometimes straight impossible. It is therefore natural to look for numerical methods such as genetic algorithm used by NNPDF for PDFs [47], stochastic gradient descent methods used by nNNPDF for nuclear PDFs [48], and trustregion methods as provided by Ceres Solver [49] and utilized by MAPFF [23] and SHKS22 [37]. In this analysis, we adopt the later method which is implemented in the MontBlanc package [36].…”

Section: A Minimization and Uncertainty Propagation Methodsmentioning

confidence: 99%

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Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data

Soleymaninia,

Hashamipour,

Khanpour

2022

Preprint

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In this paper, we present a QCD analysis to extract the Fragmentation Functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering data sets. This analysis includes the data from all available single inclusive electron-positron annihilation (SIA) processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. The SIDIS data which has been measured by the COMPASS experiment could allow the flavor dependence of the FFs to be well constrained. We exploit the analytic derivative of the Neural Network (NN) for the parametrization of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of experimental uncertainties and the Parton distribution functions (PDFs) as well. Very good agreements are achieved between the SHK22.h FFs set and the most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In addition, we discuss the impact arising from the inclusion of SIDIS data on the extracted light-charged hadron FFs. The global QCD resulting at NLO for charged hadron FFs provides valuable insights for applications in present and future high-energy measurement of charged hadron final state processes.

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Section: A Minimization and Uncertainty Propagation Methodsmentioning

confidence: 99%

“…The code is an open-source package that provides a framework for the determination of the FFs, for many different kinds of analyses in QCD. So far, it has been developed to determine the FFs of the pion from experimental data for SIA and SIDIS data sets [23], and in our most recent study to determine the fragmentation functions of Ξ − / Ξ+ [37].…”

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confidence: 99%

Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data

Soleymaninia,

Hashamipour,

Khanpour

2022

Preprint

Self Cite

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“…A milestone in deepening our understanding of tetraquark fragmentation will rely upon comparing predictions from our TQHL1.0 FFs with ones based on functions extracted from global data. Along this direction, artificial-intelligence techniques already adapted to address the collinear fragmentation of lighter-hadron species [438][439][440][441][442][443][444][445] will be an asset.…”

Section: Final Remarksmentioning

confidence: 99%

Vector Quarkonia at the LHC with Jethad: A High-Energy Viewpoint

Celiberto

2023

Universe

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In this review, we discuss and extend the study of the inclusive production of vector quarkonia, J/ψ and Υ, emitted with large transverse momenta and rapidities at the LHC. We adopt the novel ZCW19+ determination of fragmentation functions to depict the quarkonium production mechanism at the next-to-leading level of perturbative QCD. This approach is based on the nonrelativistic QCD formalism well adapted to describe the formation of a quarkonium state from the collinear fragmentation of a gluon or a constituent heavy quark at the lowest energy scale. We rely upon the NLL/NLO+ hybrid high-energy and collinear factorization for differential cross-sections, where the collinear formalism is enhanced by the BFKL resummation of next-to-leading energy logarithms arising in the t-channel. We employ the method to analyze the behavior of the rapidity distributions for double-inclusive vector quarkonium and inclusive vector quarkonium plus jet emissions. We discover that the natural stability of the high-energy series, previously seen in observables sensitive to the emission of hadrons with heavy flavor detected in the rapidity acceptance of LHC barrel calorimeters, becomes even more manifest when these particles are tagged in forward regions covered by endcaps. Our findings present the important message that vector quarkonia at the LHC via hybrid factorization offer a unique chance to perform precision studies of high-energy QCD, as well as an intriguing opportunity to shed light on the quarkonium production puzzle.

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Fragmentation Functions for $Ξ^-/\barΞ^+$ Using Neural Networks

Cited by 2 publications

References 44 publications

Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data

Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data

Vector Quarkonia at the LHC with Jethad: A High-Energy Viewpoint

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