STI Publications - View Publication Form #18087

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Publication Information
Title One-loop fermion-photon vertex in arbitrary gauge and dimensions: a novel approach
Abstract We compute one-loop electron-photon vertex with fully off-shell external momenta in an arbitrary covariant gauge and space-time dimension. There exist numerous efforts in literature where one-loop off-shell vertex is calculated by employing the standard first order Feynman rules in different covariant gauges and space-time dimensions of interest. The tensor structure which decomposes this three-point vertex into the components transverse and longitudinal to the photon momentum gets intertwined in this first order formalism. The Ward-Takahashi identity is explicitly invoked to untangle the two pieces and the results are expressed in a preferred basis of twelve spin-amplitudes. We propose a novel approach based upon an efficient combination of the first and second order formalisms of quantum electrodynamics to compute this one-loop vertex. Among some conspicuous advantages is the fact that this less known second order formalism separates the spin and scalar degrees of freedom of an elect
Author(s) Victor Miguel Banda Guzmán, Adnan Bashir
Publication Date April 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, CIC - University of Michoacan
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-THY-23-3790 OSTI Number: 1971525
LANL Number: arxiv:2304.03719 Other Number: DOE/OR/23177-5981
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
Issue
Page(s) 073008
Attachments/Datasets/DOI Link
Document(s)
QED-Vertex-Lanl.pdf (STI Document)
PhysRevD.107.073008.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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