Hadron Spectroscopy: Light, Strange Baryons

IF 1.7 4区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Few-Body Systems Pub Date : 2024-05-26 DOI:10.1007/s00601-024-01933-1

Chandni Menapara, Ajay Kumar Rai

引用次数: 0

Abstract

The resonance mass spectra have been studied through a non-relativistic hypercentral Constituent Quark Model using a linear potential. Also, the effects of higher order correction terms (\({\mathcal {O}}(\frac{1}{m})\), \({\mathcal {O}}(\frac{1}{m^{2}})\)) have been studied for improvisation of the results. Other baryonic properties such as Regge trajectories, magnetic moment and decay widths have been considered. A detailed comparison with other approaches are discussed in the present review

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强子光谱学：光、奇异重子

通过使用线性势的非相对论超中心成分夸克模型研究了共振质谱。此外，还研究了高阶修正项（\({\mathcal {O}}(\frac{1}{m})\), \({\mathcal {O}}(\frac{1}{m^{2}}) ）对改进结果的影响。还考虑了其他重子特性，如雷格轨迹、磁矩和衰变宽度。本综述讨论了与其他方法的详细比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Few-Body Systems 物理-物理：综合

CiteScore

2.90

自引率

18.80%

发文量

审稿时长

6-12 weeks

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).