Data-Driven Extraction of Hadron Radii

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

Few-Body Systems Pub Date : 2023-10-27 DOI:10.1007/s00601-023-01865-2

Daniele Binosi

引用次数: 0

Abstract

We describe the statistical Schlessinger Point Method. Grounded in analytic function theory, this method returns an objective assessment of the information contained in any data under consideration, which is independent of assumptions about underlying dynamics, and thus free from practitioner-induced bias. As a test to its robust nature and versatility, we apply it to a fully data-driven extraction of the proton, pion, and deuteron radii.

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强子半径的数据驱动提取

我们描述了统计Schlessinger点法。该方法以解析函数理论为基础，对所考虑的任何数据中包含的信息进行客观评估，这独立于对潜在动态的假设，因此不受从业者引起的偏见的影响。作为对其健壮性和多功能性的测试，我们将其应用于质子，介子和氘核半径的完全数据驱动提取。

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).

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