Sergey Syritsyn, Michael Engelhardt, Jeremy Green, Stefan Krieg, John Negele, Andrew Pochinsky
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Nucleon Electromagnetic Form Factors at Large Momentum Transfer from Lattice QCD
Nucleon form factors at large momentum transfer are important for understanding the transition from nonperturbative to perturbative QCD and have been the focus of experiment and phenomenology. We calculate proton and neutron electromagnetic form factors \(G_{E,M}(Q^2)\) from first principles using nonperturbative methods of lattice QCD. We have accumulated large Monte Carlo statistics to study form factors up to momentum transfer \(Q^2\lesssim 8\text { GeV}^2\) with a range of lattice spacings as well as quark masses that approach the physical point. In this paper, results of initial analyses are presented and compared to experiment, and potential sources of systematic uncertainty are discussed.
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).