×
近期发现有不法分子冒充我刊与作者联系,借此进行欺诈等不法行为,请广大作者加以鉴别,如遇诈骗行为,请第一时间与我刊编辑部联系确认(《中国物理C》(英文)编辑部电话:010-88235947,010-88236950),并作报警处理。
本刊再次郑重声明:
(1)本刊官方网址为cpc.ihep.ac.cn和https://iopscience.iop.org/journal/1674-1137
(2)本刊采编系统作者中心是投稿的唯一路径,该系统为ScholarOne远程稿件采编系统,仅在本刊投稿网网址(https://mc03.manuscriptcentral.com/cpc)设有登录入口。本刊不接受其他方式的投稿,如打印稿投稿、E-mail信箱投稿等,若以此种方式接收投稿均为假冒。
(3)所有投稿均需经过严格的同行评议、编辑加工后方可发表,本刊不存在所谓的“编辑部内部征稿”。如果有人以“编辑部内部人员”名义帮助作者发稿,并收取发表费用,均为假冒。
                  
《中国物理C》(英文)编辑部
2024年10月30日

Covariance Propagation in R-Matrix Model Fitting

  • This work is done for improving the current international standard cross section of nuclear reaction. The features of covariance propagation in R-matrix model fitting for 7Li,11B and 16O systems are researched systematically with Code RAC, and the results about propagation of non-diagonal elements of covariance matrix are presented. It is found that in R-matrix model fitting, short-energy-range parameters result in relatively smaller covariance propagation coefficient (CPC), medium and long-energy-range parameters produce relatively larger CPC. Especially the medium-energy-range component of systematic error plays very important role in propagation of covariance. In the evaluation procedure of nuclear data both long-energy-range component (LERC) and medium-energy-range component (MERC) of systematic error should be considered in experimental data-base file. Furthermore, these conclusions are suitable for the similar model fitting in other science fields.
  • 加载中
  • [1] .Carlson A D,Muir D W,Pronyaev V G.2001,IAEA,INDC (NDS)24252.ZHANG Feng,KONG Xiang-Zhong.High Energy Phys.and Nucl.Phys.,2003,27 (1):28 (in Chinese)(张锋,孔祥忠.高能物理与核物理,2003,27(1):28)3.CHEN Zhen-Peng,ZHANG Rui,SUN Ye2Ying et al.Science in China,2003,G46(3):2554.Smith D L.Probability.Statistics and Data Uncertainties in NuclearScience and Technology,American Nuclear Society,Inc.1991,229 —2325.CHEN Zhen-Peng,SUN Ye-Ying.IAEA,2003,INDC(NDS)2438:626.Lane A M,Thomas R G.Reviews of Modern Physics,1958,30 (2):257
  • 加载中

Get Citation
CHEN Zhen-Peng, SUN Ye-Ying, ZHANG Rui and LIU Ting-Jin. Covariance Propagation in R-Matrix Model Fitting[J]. Chinese Physics C, 2004, 28(1): 42-47.
CHEN Zhen-Peng, SUN Ye-Ying, ZHANG Rui and LIU Ting-Jin. Covariance Propagation in R-Matrix Model Fitting[J]. Chinese Physics C, 2004, 28(1): 42-47. shu
Milestone
Received: 2003-05-26
Revised: 1900-01-01
Article Metric

Article Views(3261)
PDF Downloads(593)
Cited by(0)
Policy on re-use
To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.
通讯作者: 陈斌, [email protected]
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Email This Article

Title:
Email:

Covariance Propagation in R-Matrix Model Fitting

    Corresponding author: CHEN Zhen-Peng,
  • Department of Physics,Tsinghua University,Beijing 100084,China2 China Nuclear Data Center,Beijing 102413,China

Abstract: This work is done for improving the current international standard cross section of nuclear reaction. The features of covariance propagation in R-matrix model fitting for 7Li,11B and 16O systems are researched systematically with Code RAC, and the results about propagation of non-diagonal elements of covariance matrix are presented. It is found that in R-matrix model fitting, short-energy-range parameters result in relatively smaller covariance propagation coefficient (CPC), medium and long-energy-range parameters produce relatively larger CPC. Especially the medium-energy-range component of systematic error plays very important role in propagation of covariance. In the evaluation procedure of nuclear data both long-energy-range component (LERC) and medium-energy-range component (MERC) of systematic error should be considered in experimental data-base file. Furthermore, these conclusions are suitable for the similar model fitting in other science fields.

    HTML

Reference (1)

目录

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return