{"title":"在 PGNAA 中建模和绘制几何图形","authors":"","doi":"10.1016/j.cpc.2024.109360","DOIUrl":null,"url":null,"abstract":"<div><p>Prompt Gamma Neutron Analysis Activation is a widely used technique for analyzing materials. This technique defines graphs (reference spectrum collection, or libraries) of spectral intensity as a function of energy (channels) for the elements inserted in a sample. The Monte Carlo Library Least Squares (MCLLS) is the dominant approach in the PGNAA technique. The main difficulties faced in the MCLLS domain are (1) numerical instabilities in the least-squares stage (Library Least Squares (LLS)); (2) overdetermination of the system of equations; (3) linear dependence in the libraries; (4) gamma radiation scattering; (5) high computational costs. The present work proposes optimizing the LLS module to face the abovementioned problems using the Greedy Randomized Adaptive Search Procedure (GRASP) and Continuous Greedy Randomized Adaptive Search Procedure (CGRASP) algorithms. The search for the spectral count peaks of the libraries leads to a partitioning of the data before applying the GRASP and CGRASP algorithms. The methodological procedures also address estimating the spectral counts of an unknown library possibly integrates the sample. The results show (1) efficient partitioning of the input data (2) evidence of suitable precision of the weight fractions of the libraries that make up the sample (average precision of the order of 3.16% against 8.8% of other methods); (3) success in the approximation and estimation of the unknown library (average precision of 4.25%) present in the sample. Our method proved to be promising in improving the determination of percentage count fractions by the least-squares module and showing the advantages of data partitioning.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling and geometrization in PGNAA\",\"authors\":\"\",\"doi\":\"10.1016/j.cpc.2024.109360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Prompt Gamma Neutron Analysis Activation is a widely used technique for analyzing materials. This technique defines graphs (reference spectrum collection, or libraries) of spectral intensity as a function of energy (channels) for the elements inserted in a sample. The Monte Carlo Library Least Squares (MCLLS) is the dominant approach in the PGNAA technique. The main difficulties faced in the MCLLS domain are (1) numerical instabilities in the least-squares stage (Library Least Squares (LLS)); (2) overdetermination of the system of equations; (3) linear dependence in the libraries; (4) gamma radiation scattering; (5) high computational costs. The present work proposes optimizing the LLS module to face the abovementioned problems using the Greedy Randomized Adaptive Search Procedure (GRASP) and Continuous Greedy Randomized Adaptive Search Procedure (CGRASP) algorithms. The search for the spectral count peaks of the libraries leads to a partitioning of the data before applying the GRASP and CGRASP algorithms. The methodological procedures also address estimating the spectral counts of an unknown library possibly integrates the sample. The results show (1) efficient partitioning of the input data (2) evidence of suitable precision of the weight fractions of the libraries that make up the sample (average precision of the order of 3.16% against 8.8% of other methods); (3) success in the approximation and estimation of the unknown library (average precision of 4.25%) present in the sample. Our method proved to be promising in improving the determination of percentage count fractions by the least-squares module and showing the advantages of data partitioning.</p></div>\",\"PeriodicalId\":285,\"journal\":{\"name\":\"Computer Physics Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Physics Communications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010465524002832\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Physics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010465524002832","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Prompt Gamma Neutron Analysis Activation is a widely used technique for analyzing materials. This technique defines graphs (reference spectrum collection, or libraries) of spectral intensity as a function of energy (channels) for the elements inserted in a sample. The Monte Carlo Library Least Squares (MCLLS) is the dominant approach in the PGNAA technique. The main difficulties faced in the MCLLS domain are (1) numerical instabilities in the least-squares stage (Library Least Squares (LLS)); (2) overdetermination of the system of equations; (3) linear dependence in the libraries; (4) gamma radiation scattering; (5) high computational costs. The present work proposes optimizing the LLS module to face the abovementioned problems using the Greedy Randomized Adaptive Search Procedure (GRASP) and Continuous Greedy Randomized Adaptive Search Procedure (CGRASP) algorithms. The search for the spectral count peaks of the libraries leads to a partitioning of the data before applying the GRASP and CGRASP algorithms. The methodological procedures also address estimating the spectral counts of an unknown library possibly integrates the sample. The results show (1) efficient partitioning of the input data (2) evidence of suitable precision of the weight fractions of the libraries that make up the sample (average precision of the order of 3.16% against 8.8% of other methods); (3) success in the approximation and estimation of the unknown library (average precision of 4.25%) present in the sample. Our method proved to be promising in improving the determination of percentage count fractions by the least-squares module and showing the advantages of data partitioning.
期刊介绍:
The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper.
Computer Programs in Physics (CPiP)
These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged.
Computational Physics Papers (CP)
These are research papers in, but are not limited to, the following themes across computational physics and related disciplines.
mathematical and numerical methods and algorithms;
computational models including those associated with the design, control and analysis of experiments; and
algebraic computation.
Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.