Best reaction target to determine proton distribution radii of atomic nuclei

📅 2026-08-12
🏛️ Physical Review Letters
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
本文通过使用铅等重靶标进行电荷变化截面测量,解决了不稳定原子核质子分布半径测定问题,发现重靶比低-Z靶更适合。
📝 Abstract
We found that a heavy target such as Pb is most suitable for determining the proton distribution radii of unstable nuclei through charge-changing cross-section ($\sigma_\text{cc}$) measurements. As a heavy ion probe, low-$Z$ targets are routinely used to determine nucleon distribution radii of unstable isotopes. This approach has recently been extended to study proton distribution radii from $\sigma_\text{cc}$ measurements. However, empirical scaling factors have to be introduced to apply the Glauber models. In the present work, we systematically investigated the scaling factor using 39 new $\sigma_\text{cc}$ data of 18 $p$-shell nuclei on hydrogen, carbon, silver, and lead targets at around 240 MeV/nucleon. Together with the existing data, we reveal a universal dependence of the scaling factor on both the masses of target nuclei and the separation energies of projectile nuclei. The scaling factors decrease with increasing target-nucleus mass and converge to 1 for the highest-$Z$ target, making the scaling unnecessary. We conclude that instead of a low-$Z$ target, employing a heavy target such as Pb in $\sigma_\text{cc}$ measurements is the best option to determine the proton distribution radii of unstable nuclei.
Problem

Research questions and friction points this paper is trying to address.

proton distribution radii
unstable nuclei
charge-changing cross-section
Glauber models
scaling factor
Innovation

Methods, ideas, or system contributions that make the work stand out.

proton distribution radii
charge-changing cross-section
scaling factor
heavy target
🔎 Similar Papers
J
Jun-Yao Xu
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
B
Bao-Hua Sun
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
Isao Tanihata
Isao Tanihata
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
S
Satoru Terashima
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
J
Jian-Wei Zhao
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
J
Ji-Chao Zhang
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
G
Ge Guo
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
S
Shi-Tao Wang
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
L
Lei Shen
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
J
Jun Su
Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai 519082, China
X
Xiao-Dong Xu
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
A
Andrej Prochazka
Medaustron, 2700 Wiener Neustadt, Austria
G
Guang-Shuai Li
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
X
Xiu-Lin Wei
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
C
Chang-Jian Wang
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
Feng Wang
Feng Wang
Beijing Jiaotong University
PrognosticsApplied statisticsDeep learningPHMRailway equipment
M
Meng Wang
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
Jing Wang
Jing Wang
South China Normal University
nanomaterialselectrocatalysisphotocatalysis
L
Liu-Chun He
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
C
Chuan-Ye Liu
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
W
Wen-Jian Lin
School of Physics, Beihang University, 100191 Beijing, People’s Republic of China
W
Wei-Ping Lin
Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, China
Zhong Liu
Zhong Liu
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
P
Pei-Pei Ren
Key Laboratory of Radiation Physics and Technology of the Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, China
Yu Zhang
Yu Zhang
Beihang University
surgical planningimage processingmachine learning