Jai More, Asmita Mukherjee, Sreeraj Nair, Sudeep Saha
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引用次数: 0
Abstract
We consider a light-front dressed quark state, per se, instead of a proton state, we consider a simple composite spin-1/2 state of a quark dressed with a gluon. This perturbative model incorporates gluonic degrees of freedom, which enable us to evaluate the gravitational form factors (GFFs) of the quark as well as the gluon in this model (More et al. Phys Rev D 105(5):056017, 2022. arXiv:2112.06550, https://doi.org/10.1103/PhysRevD.105.056017; Gluon contribution to the mechanical properties of a dressed quark in light-front Hamiltonian QCD, 2023. arXiv:2302.11906). We employ the Hamiltonian framework and choose the light-front gauge \(A^+=0\). We calculate the four GFFs and corroborate the sum rules that GFFs satisfy. The GFF DD is attributed to information like pressure, shear, and energy distributions. We analyze some of these distributions for a dressed quark state at one loop in QCD.
期刊介绍:
The journal Few-Body Systems presents original research work – experimental, theoretical and computational – investigating the behavior of any classical or quantum system consisting of a small number of well-defined constituent structures. The focus is on the research methods, properties, and results characteristic of few-body systems. Examples of few-body systems range from few-quark states, light nuclear and hadronic systems; few-electron atomic systems and small molecules; and specific systems in condensed matter and surface physics (such as quantum dots and highly correlated trapped systems), up to and including large-scale celestial structures.
Systems for which an equivalent one-body description is available or can be designed, and large systems for which specific many-body methods are needed are outside the scope of the journal.
The journal is devoted to the publication of all aspects of few-body systems research and applications. While concentrating on few-body systems well-suited to rigorous solutions, the journal also encourages interdisciplinary contributions that foster common approaches and insights, introduce and benchmark the use of novel tools (e.g. machine learning) and develop relevant applications (e.g. few-body aspects in quantum technologies).