18–20 Nov 2024
JLab
America/New_York timezone

Session

Reception and poster session

18 Nov 2024, 18:00
JLab

JLab

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  1. Yushan Su (University of Maryland)
    18/11/2024, 18:00
    Poster

    Parton distribution functions (PDFs) at large $x$ are difficult to be extracted from experimental data, but are extremely important in understanding hadron structures as well as searching for new physics beyond the Standard Model. We study the large $x$ PDFs under the framework of large momentum $P^z$ expansion of lattice quasi-PDFs. In the threshold limit, the matching kernel of quasi-PDF can...

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  2. Rui Zhang
    18/11/2024, 18:15
    Poster

    Measuring hadrons with highly boosted momentum are necessary for the calculation of parton physics and form factors from lattice QCD. However,
    the worsening signal-to-noise has been one of the biggest problems for simulating boosted hadron states on the lattice, preventing us from accessing the form factors at very large $Q^2$ or parton physics with a largely suppressed $1/P^n_z$ power...

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  3. Kehfei Liu
    18/11/2024, 18:30
    Poster

    We present the determination of the nucleon’s Sachs electric form factor using the hadronic tensor formalism and verify that it is consistent with that from the conventional three-point function calculation. We additionally obtain the transition form factor from the nucleon to its first radial excited tate within a finite volume. Consequently, we identify the latter with the nucleon-to-Roper...

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  4. Zaki Panjsheeri
    18/11/2024, 18:45
    Poster

    One of the core challenges for the investigation of hadronic structure through exclusive processes is the inverse problem. Traditionally, the problem is considered to be one of inverting the convolution of the Compton form factors (CFFs) with the Wilson coefficient function in order to extract generalized parton distributions (GPDs). However, the EXCLAIM collaboration has devised a method for...

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  5. Adil Umar Khawaja (University of Virginia)
    18/11/2024, 19:00
    Poster

    Generalized parton distibutions (GPDs) are a key construct for understanding the spatial distribution of quarks and gluons inside nucleons. These distributions are accessed through deeply virtual exclusive processes, the cross sections of which are parameterized using a class of observables known as Compton Form Factors (CFFs). We present a spectator model-based parameterization of twist 2...

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  6. Saraswati Pandey (Department of Physics, University of Virginia)
    18/11/2024, 19:15
    Poster

    From polarised Deep Inelastic scattering (DIS) asymmetries, and cross-section data, it is possible to extract the polarized structure functions $g_1$ and $g_2$. In the parton model picture of proton, the structure function $g_1^p$ is expressed in terms of $g_1^p = \Delta \Sigma + \Delta G$, the net quark and gluon helicity contributions to the proton spin. This spin structure function can also...

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  7. Ho Jang (University of Virginia)
    18/11/2024, 19:30
    Poster

    Generalized parton distributions (GPDs) provide information about the internal structure of the proton, but GPDs do not enter cross sections directly; instead, they enter through, e.g., the Compton form factors (CFFs), which are the observables of deeply virtual Compton scattering (DVCS). Additionally, one of the most physically interesting quantities in hadron structure is the angular...

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  8. Andrew Dotson (New Mexico State University), Anusha Singireddy (Old Dominion University)
    18/11/2024, 19:45
    Poster

    AI/ML informed Symbolic Regression is the next stage of scientific modeling. We utilize a highly customizable symbolic regression package "PySR" to model the x and t dependence of the flavor isovector combination $H^{u-d}(x,t,ζ,Q^2)$ at ζ=0 and $Q^2$= 4 GeV$^2$. These PySR models were trained on GPD pseudodata provided by both Lattice QCD and phenomenological sources GGL, GK, and VGG. Symbolic...

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  9. Emmanuel Ortiz-Pacheco (Michigan State University), Dr Liam Hockley (New Mexico State University)
    18/11/2024, 20:00
    Poster

    We present an analysis of Machine Learning techniques applied to the determination of ratios of nucleon 3-point and 2-point correlation functions in Euclidean time. These quantities are typically computed in the regime of Lattice QCD and are of interest in determining various measures of hadronic structure, such as parton distribution functions (PDFs) and generalised parton distributions...

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  10. Christian Zimmermann
    18/11/2024, 20:15
    Poster

    We calculate Euclidean hadronic matrix elements of two spatially separated local quark currents in position space, in order to determine the $x$ dependence of parton distribution functions (PDFs). This approach is often referred to by the term lattice cross section (LCS). We extend previous works on that topic by considering valence quark PDFs of the proton and adapt the formalism to our...

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