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Title: MONTE CARLO ERROR ESTIMATION APPLIED TO NONDESTRUCTIVE ASSAY METHODS

Monte Carlo randomization of nuclear counting data into N replicate sets is the basis of a simple and effective method for estimating error propagation through complex analysis algorithms such as those using neural networks or tomographic image reconstructions. The error distributions of properly simulated replicate data sets mimic those of actual replicate measurements and can be used to estimate the std. dev. for an assay along with other statistical quantities. We have used this technique to estimate the standard deviation in radionuclide masses determined using the tomographic gamma scanner (TGS) and combined thermal/epithermal neutron (CTEN) methods. The effectiveness of this approach is demonstrated by a comparison of our Monte Carlo error estimates with the error distributions in actual replicate measurements and simulations of measurements. We found that the std. dev. estimated this way quickly converges to an accurate value on average and has a predictable error distribution similar to N actual repeat measurements. The main drawback of the Monte Carlo method is that N additional analyses of the data are required, which may be prohibitively time consuming with slow analysis algorithms.
Authors:
;
Publication Date:
OSTI Identifier:
769070
Report Number(s):
LA-UR-00-2164
TRN: US0102603
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Jun 2000
Research Org:
Los Alamos National Lab., NM (US)
Sponsoring Org:
US Department of Energy (US)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; MONTE CARLO METHOD; NEUTRON DETECTION; GAMMA DETECTION; RADIOISOTOPE SCANNING; DATA ANALYSIS; DATA COVARIANCES; NONDESTRUCTIVE ANALYSIS