Using the Monte-Carlo method to analyze experimental data and produce uncertainties and covariances

被引：0

作者：

Henning, Greg ^{[1
]}

Kerveno, Maelle ^{[1
]}

Dessagne, Philippe ^{[1
]}

Claeys, Francois ^{[1
,2
]}

Bako, Nicolas Dari ^{[1
]}

Dupuis, Marc ^{[3
]}

Hilaire, Stephane ^{[3
]}

Romain, Pascal ^{[3
]}

Saint Jean, Cyrille De

Capote, Roberto ^{[4
]}

Boromiza, Marian ^{[5
]}

Olacel, Adina ^{[5
]}

Negret, Alexandru ^{[5
]}

Borcea, Catalin ^{[5
]}

Plompen, Arjan ^{[6
]}

Dobarro, Carlos Paradela ^{[6
]}

Nyman, Markus ^{[6
]}

Drohe, Jean-Claude ^{[6
]}

Wynants, Ruud ^{[6
]}

机构：

[1] Univ Strasbourg, CNRS, IPHC DRS UMR 7178, 23 Rue Loess, F-67037 Strasbourg, France

[2] CEA, DES, IRESNE, DER SPRC LEPh, F-13108 St Paul Les Durance, France

[3] CEA, DAM, DIF, F-91297 Arpajon, France

[4] Nucl Data Sect, Int Atom Energy Agcy, Wagramer Str, A-1400 Vienna, Austria

[5] Horia Hulubei Natl Inst Phys & Nucl Engn, Bucharest 077125, Romania

[6] Joint Res Ctr, European Commiss, Retieseweg 111, B-2440 Geel, Belgium

来源：

15TH INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY, ND2022 | 2023年 / 284卷

关键词：

D O I：

10.1051/epjconf/202328401045

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

The production of useful and high-quality nuclear data requires measurements with high precision and extensive information on uncertainties and possible correlations. Analytical treatment of uncertainty propagation can become very tedious when dealing with a high number of parameters. Even worse, the production of a covariance matrix, usually needed in the evaluation process, will require lenghty and error-prone formulas. To work around these issues, we propose using random sampling techniques in the data analysis to obtain final values, uncertainties and covariances and for analyzing the sensitivity of the results to key parameters. We demonstrate this by one full analysis, one partial analysis and an analysis of the sensitivity to branching ratios in the case of (n,n'gamma) cross section measurements.

引用

页数：4

共 50 条

[31] MANIPULATOR WORKSPACE ANALYSIS USING THE MONTE-CARLO METHOD
RASTEGAR, J
FARDANESH, B
MECHANISM AND MACHINE THEORY, 1990, 25 (02) : 233 - 239
[32] GEOMETRIC SYNTHESIS OF MANIPULATORS USING THE MONTE-CARLO METHOD
RASTEGAR, J
FARDANESH, B
JOURNAL OF MECHANICAL DESIGN, 1990, 112 (03) : 450 - 452
[33] CONFORMATIONAL TRANSITION IN ALKANES, USING MONTE-CARLO METHOD
KHALATUR, PG
ZHURNAL FIZICHESKOI KHIMII, 1979, 53 (05): : 1293 - 1295
[34] NEUTRON SPECTRAL UNFOLDING USING THE MONTE-CARLO METHOD
OBRIEN, K
SANNA, R
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH, 1981, 185 (1-3): : 277 - 286
[35] EXPERIMENTAL SIMULATION OF LARGE-SCALE BIOREACTOR ENVIRONMENTS USING A MONTE-CARLO METHOD
NAMDEV, PK
YEGNESWARAN, PK
THOMPSON, BG
GRAY, MR
CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 1991, 69 (02): : 513 - 519
[36] ESTIMATION OF UNCERTAINTIES IN STOPPING-POWER RATIOS USING MONTE-CARLO METHODS
ANDREO, P
FRANSSON, A
APPLIED RADIATION AND ISOTOPES, 1992, 43 (11) : 1425 - 1426
[37] MONTE-CARLO METHOD APPLIED IN PROCESSING THE ELECTROCHEMICAL MEASUREMENT DATA
TONG, JJ
XIE, SF
HUANG, ZQ
JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 1987, 134 (8B) : C507 - C507
[38] VALIDITY OF EXPANDED UNCERTAINTIES EVALUATED USING THE MONTE CARLO METHOD
Tanaka, Hideyuki
Ehara, Kensei
ADVANCED MATHEMATICAL AND COMPUTATIONAL TOOLS IN METROLOGY AND TESTING VIII, 2009, 78 : 326 - 329
[39] Multivariate forecast error covariances for an ocean model estimated by Monte-Carlo simulation
Borovikov, A
Rienecker, MM
SYMPOSIUM ON OBSERVATIONS, DATA ASSIMILATION, AND PROBABILISTIC PREDICTION, 2002, : 82 - 85
[40] A MONTE-CARLO STUDY FOR EXAMINATION OF COVARIANCES BETWEEN HALF SIBS AS PREDICTION PARAMETER
BELLMANN, K
AHRENS, H
BIOMETRISCHE ZEITSCHRIFT, 1969, 11 (05): : 305 - &

← 1 2 3 4 5 →