Determination of Nuclear Matter Radii by Means of Microscopic Optical Potentials: The Case of \(^{78}\)Kr

IF 1.7 4区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY Few-Body Systems Pub Date : 2024-04-24 DOI:10.1007/s00601-024-01919-z
Matteo Vorabbi, Paolo Finelli, Carlotta Giusti
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Abstract

In this work we use microscopic Nucleon–Nucleus Optical Potentials (OP) to analyze elastic scattering data for the differential cross section of the \(^{78}\)Kr (p,p) \(^{78}\)Kr reaction, with the goal of extracting the matter radius and estimating the neutron skin, quantities that are both needed to determine the slope parameter L of the nuclear symmetry energy. Our analysis is performed with the factorized version of the microscopic OP obtained in a previous series of papers within the Watson multiple scattering theory at the first order of the spectator expansion, which is based on the underlying nucleon–nucleon dynamics and is free from phenomenological inputs. Differently from our previous applications, the proton and neutron densities are described with a two-parameter Fermi (2pF) distribution, which makes the extraction of the matter radius easier and allows us to make a meaningful comparison with the original analysis, that was performed with the Glauber model. With standard minimization techniques we performed data analysis and extracted the matter radius and the neutron skin. Our analysis produces a matter radius of \(R_m^{\mathrm{(rms)}} = 4.12\) fm, in good agreement with previous matter radii extracted from \(^{76}\)Kr and \(^{80}\)Kr, and a neutron skin of \(\Delta R_{np} \simeq - 0.1\) fm, compatible with a previous analysis. Our factorized microscopic OP, supplied with 2pF densities, is a valuable tool to perform the analysis of the experimental differential cross section and extract information such as matter radius and neutron skin. Without any free parameters it provides a reasonably good description of the experimental differential cross section for scattering angles up to \(\approx \) 40 degrees. Compared to the Glauber model our OP can be applied to a wider range of scattering angles and allows one to probe the nuclear systems in a more internal region.

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利用微光学势测定核物质半径:$^{78}$$Kr的案例
在这项工作中,我们使用微观核子-核光学势(OP)来分析 \(^{78}\)Kr (p,p) \(^{78}\)Kr 反应差分截面的弹性散射数据,目的是提取物质半径和估计中子皮层,这两个量都是确定核对称性能量的斜率参数 L 所需要的。我们的分析是利用先前一系列论文中在沃森多重散射理论中获得的微观 OP 的因子化版本在旁观者扩展的一阶上进行的,它基于基本的核子-核子动力学,不受现象学输入的影响。与我们以前的应用不同,质子和中子密度是用双参数费米(2pF)分布来描述的,这使物质半径的提取变得更容易,并使我们能够与原来用格劳伯模型进行的分析进行有意义的比较。利用标准的最小化技术,我们进行了数据分析,并提取了物质半径和中子表皮。我们的分析得出的物质半径为(R_m^{\mathrm{(rms)}} = 4.12\) fm,与之前从(^{76}\)Kr 和(^{80}\)Kr 提取的物质半径非常一致;中子皮层为(\Delta R_{np} \simeq - 0.1\) fm,与之前的分析一致。我们的因数化微观 OP 提供了 2pF 密度,是对实验微分截面进行分析并提取物质半径和中子皮层等信息的重要工具。在没有任何自由参数的情况下,它对散射角高达40度的实验微分截面提供了相当好的描述。与格劳伯模型相比,我们的OP可以应用于更大范围的散射角,并允许人们在更多的内部区域探测核系统。
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来源期刊
Few-Body Systems
Few-Body Systems 物理-物理:综合
CiteScore
2.90
自引率
18.80%
发文量
64
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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