Pion GPDs: A path toward phenomenology

Chavez, Jose; Bertone, Valerio; Soto, F. De; Defurne, M.; Mezrag, Cédric; Moutarde, H.; Rodríguez‐Quintero, J.; Segovia, Jorge

doi:10.48550/arxiv.2110.06052

Cited by 4 publications

(13 citation statements)

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“…An on-going theoretical effort now provides wide families of GPD models which consistently address polynomiality and positivity properties. This allows robust predictions of new processes in as yet unexplored kinematic domains, like, e.g., DVCS off a pion target at the EIC [275,276].…”

Section: Phenomenological Extraction Of Gpds From Dvcsmentioning

confidence: 99%

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Khalek,

D'Alesio,

Arratia

et al. 2022

Preprint

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Electron Ion Collider (EIC) is a particle accelerator facility planned for construction at Brookhaven National Laboratory on Long Island, New York by the United States Department of Energy. EIC will provide capabilities of colliding beams of polarized electrons with polarized beams of proton and light ions. EIC will be one of the largest and most sophisticated new accelerator facilities worldwide, and the only new large-scale accelerator facility planned for construction in the United States in the next few decades. The versatility, resolving power and intensity of EIC will present many new opportunities to address some of the crucial and fundamental open scientific questions in particle physics. This document provides an overview of the science case of EIC from the perspective of the high energy physics community.

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Section: Phenomenological Extraction Of Gpds From Dvcsmentioning

confidence: 99%

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Khalek,

D'Alesio,

Arratia

et al. 2022

Preprint

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“…4. Still, a direct modelling of (x, ξ)-space can be used for a subset of non-singular GPD models, like the pion model [59,60] used for a demonstration in Sect. 3.4.…”

Section: Principles Of Modellingmentioning

confidence: 99%

Artificial neural network modelling of generalised parton distributions

et al. 2022

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We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments.

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“…where the quark energy in SNM 𝜖 𝐹 = √︃ 𝑘 2 𝐹 + 𝑀 * 2 𝑞 + 𝑉 0 ≡ 𝐸 𝐹 +𝑉 0 . The 𝑘 𝐹 is the quark Fermi momentum which is related to the quark density as 𝜌 𝑞 = 2𝑘 3 𝐹 /𝜋 2 . For the antiquark, it is simply given by 𝜖 𝐹 = 𝐸 𝐹 − 𝑉 0 .…”

Section: In-medium Pion and Kaon Parton Distributionsmentioning

confidence: 99%

“…Also, they are very crucial quantities for computing the cross sections for the high-energy hadron-hadron and neutrino-hadron interactions. Recently the valence quark and gluon distributions of the pseudo-scalar (PS) mesons, i.e., kaon and pion become a more attractive subject in hadronic physics community [2][3][4][5][6][7][8][9][10]. This is due to they can provide us a better understanding for the PS mesons'internal structures and the dynamics of quarks and gluons.…”

Section: Introductionmentioning

confidence: 99%

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Gluon and valence quark distributions for the pion and kaon in nuclear matter

Hutauruk,

Nam

2021

Preprint

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In this paper we study the gluon and valence quark distributions in the pion and kaon in nuclear medium for various nuclear densities as well as in vacuum within the Nambu-Jona-Lasinio (NJL) model with the help of the proper-time regularization scheme which simulates a confinement of QCD. The nuclear medium effect is also determined in the same model for the symmetric nuclear matter. We then analyze the gluon and valence quark distributions for the bound pion and kaon in symmetric nuclear matter as well as those in vacuum. We find that the valence quark and gluon distributions in vacuum have relatively good agreements with the experimental data, the lattice QCD simulations, and the JAM Monte-Carlo (MC) global fit QCD analysis. Evolving to the higher factorization scale 𝑄 = 4 GeV, the in-medium gluon and valence-quark distributions of the pion for various nuclear densities are turned out to be almost unchanged in comparison to the vacuum cases. On the contrary, for the kaon, they increase significantly with respect to the densities. Finally, we find that the vacuum gluon distribution for the kaon is smaller than that for the pion, which is consistent with other theoretical predictions. This feature holds for the in-medium gluon distribution in the nuclear density up to the saturation density.

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Pion GPDs: A path toward phenomenology

Cited by 4 publications

References 0 publications

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Artificial neural network modelling of generalised parton distributions

Gluon and valence quark distributions for the pion and kaon in nuclear matter

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