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Publication Information
Title Electromagnetic Form Factors and Charge Radii of Pseudoscalar and Scalar Mesons: A Comprehensive Contact Interaction Analysis
Abstract We carry out a comprehensive survey of electromagnetic form factors of all light, heavy and heavy-light ground-state pseudoscalar and scalar mesons. Our analysis is based upon a Schwinger-Dyson equations treatment of a vector $\times$ vector contact interaction. It incorporates confinement and ensures axial vector and vector Ward-Takahashi identities are satisfied along with the corresponding corollaries such as the Goldberger-Treiman relations. The algebraic simplicity of the model allows us to compute the form factors at arbitrarily large virtualities of the probing photon momentum squared with relative ease. Wherever possible and insightful, we compare our results for the electromagnetic form factors and the charge radii with those obtained earlier through Schwinger-Dyson equations, lattice and with experimental observations available. We also comment on the scope and shortcomings of the model.
Author(s) A. Bashir, R. Hernández-Pinto, M. A. Bedolla, I. M. Higuera-Angulo
Publication Date March 2023
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Theory & Comp Physics / THEORY CENTER / THEORY CENTER
Funding Source Nuclear Physics (NP), Fulbright-García Robles scholarship, Conacyt - Mexico, CIC - University of Michoacan
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-THY-23-3744 OSTI Number: 1959187
LANL Number: arXiv:2301.11881 Other Number: DOE/OR/23177-5703
Associated with an experiment No
Associated with EIC Yes
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Physical Review D
Refereed Yes
Volume 107
Issue
Page(s) 054002
Attachments/Datasets/DOI Link
Document(s)
PSForm-Factors-lanl.pdf (STI Document)
PhysRevD.107.054002.pdf (Accepted Manuscript)
PhysRevD.107.054002.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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