Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301

Abstract This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The resp...

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Veröffentlicht in:Radiation protection dosimetry 2024-11, Vol.200 (19), p.1867-1873
Hauptverfasser: Bo, Wan, Gang, Li, Kun, Li, Qichang, Huang, Bangping, Xiong, Jiao, Cai, Jiaji, He, Wenbin, Wei, Yuan, Xia, Daibo, Yang
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container_end_page 1873
container_issue 19
container_start_page 1867
container_title Radiation protection dosimetry
container_volume 200
creator Bo, Wan
Gang, Li
Kun, Li
Qichang, Huang
Bangping, Xiong
Jiao, Cai
Jiaji, He
Wenbin, Wei
Yuan, Xia
Daibo, Yang
description Abstract This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.
doi_str_mv 10.1093/rpd/ncae189
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The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.</description><identifier>ISSN: 0144-8420</identifier><identifier>ISSN: 1742-3406</identifier><identifier>EISSN: 1742-3406</identifier><identifier>DOI: 10.1093/rpd/ncae189</identifier><identifier>PMID: 39279269</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Californium - analysis ; Computer Simulation ; Monte Carlo Method ; Neural Networks, Computer ; Neutrons ; Radiation Dosage ; Scintillation Counting - instrumentation ; Scintillation Counting - methods</subject><ispartof>Radiation protection dosimetry, 2024-11, Vol.200 (19), p.1867-1873</ispartof><rights>The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c208t-795a857dfe06d8b083267ba0d2a0b48f910e80889a5b42ade3ff41fd6538d08a3</cites><orcidid>0009-0007-4529-2907</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1583,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39279269$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bo, Wan</creatorcontrib><creatorcontrib>Gang, Li</creatorcontrib><creatorcontrib>Kun, Li</creatorcontrib><creatorcontrib>Qichang, Huang</creatorcontrib><creatorcontrib>Bangping, Xiong</creatorcontrib><creatorcontrib>Jiao, Cai</creatorcontrib><creatorcontrib>Jiaji, He</creatorcontrib><creatorcontrib>Wenbin, Wei</creatorcontrib><creatorcontrib>Yuan, Xia</creatorcontrib><creatorcontrib>Daibo, Yang</creatorcontrib><title>Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301</title><title>Radiation protection dosimetry</title><addtitle>Radiat Prot Dosimetry</addtitle><description>Abstract This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.</description><subject>Californium - analysis</subject><subject>Computer Simulation</subject><subject>Monte Carlo Method</subject><subject>Neural Networks, Computer</subject><subject>Neutrons</subject><subject>Radiation Dosage</subject><subject>Scintillation Counting - instrumentation</subject><subject>Scintillation Counting - methods</subject><issn>0144-8420</issn><issn>1742-3406</issn><issn>1742-3406</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kEtLxDAUhYMozji6ci9diSB1bh7tJEsZxxcDbnRd0iaRaNvUJEX892ac0aWre7h8fBwOQqcYrjAIOveDmveN1JiLPTTFC0ZyyqDcR1PAjOWcEZigoxDeAMhCFOwQTahIiZRiityNjtp3tpfRuj5zJuv1GH2KYdBN9GOX1TJolaWP9NEa21jZbiD_c-Kn8-_ZGGz_mrX2Y7QqC43to23brVElfxOdz1aPOQV8jA6MbIM-2d0ZerldPS_v8_XT3cPyep03BHjMU0_Ji4UyGkrFa-CUlItagiISasaNwKA5cC5kUTMilabGMGxUWVCugEs6Qxdb7-Ddx6hDrDobGp1a9dqNoaIYCiYIFzihl1u08S4Er001eNtJ_1VhqDYLV2nhardwos924rHutPpjfydNwPkWcOPwr-kbKN-Gtg</recordid><startdate>20241118</startdate><enddate>20241118</enddate><creator>Bo, Wan</creator><creator>Gang, Li</creator><creator>Kun, Li</creator><creator>Qichang, Huang</creator><creator>Bangping, Xiong</creator><creator>Jiao, Cai</creator><creator>Jiaji, He</creator><creator>Wenbin, Wei</creator><creator>Yuan, Xia</creator><creator>Daibo, Yang</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0009-0007-4529-2907</orcidid></search><sort><creationdate>20241118</creationdate><title>Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301</title><author>Bo, Wan ; Gang, Li ; Kun, Li ; Qichang, Huang ; Bangping, Xiong ; Jiao, Cai ; Jiaji, He ; Wenbin, Wei ; Yuan, Xia ; Daibo, Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c208t-795a857dfe06d8b083267ba0d2a0b48f910e80889a5b42ade3ff41fd6538d08a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Californium - analysis</topic><topic>Computer Simulation</topic><topic>Monte Carlo Method</topic><topic>Neural Networks, Computer</topic><topic>Neutrons</topic><topic>Radiation Dosage</topic><topic>Scintillation Counting - instrumentation</topic><topic>Scintillation Counting - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bo, Wan</creatorcontrib><creatorcontrib>Gang, Li</creatorcontrib><creatorcontrib>Kun, Li</creatorcontrib><creatorcontrib>Qichang, Huang</creatorcontrib><creatorcontrib>Bangping, Xiong</creatorcontrib><creatorcontrib>Jiao, Cai</creatorcontrib><creatorcontrib>Jiaji, He</creatorcontrib><creatorcontrib>Wenbin, Wei</creatorcontrib><creatorcontrib>Yuan, Xia</creatorcontrib><creatorcontrib>Daibo, Yang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Radiation protection dosimetry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bo, Wan</au><au>Gang, Li</au><au>Kun, Li</au><au>Qichang, Huang</au><au>Bangping, Xiong</au><au>Jiao, Cai</au><au>Jiaji, He</au><au>Wenbin, Wei</au><au>Yuan, Xia</au><au>Daibo, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301</atitle><jtitle>Radiation protection dosimetry</jtitle><addtitle>Radiat Prot Dosimetry</addtitle><date>2024-11-18</date><risdate>2024</risdate><volume>200</volume><issue>19</issue><spage>1867</spage><epage>1873</epage><pages>1867-1873</pages><issn>0144-8420</issn><issn>1742-3406</issn><eissn>1742-3406</eissn><abstract>Abstract This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>39279269</pmid><doi>10.1093/rpd/ncae189</doi><tpages>7</tpages><orcidid>https://orcid.org/0009-0007-4529-2907</orcidid></addata></record>
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source MEDLINE; Oxford University Press Journals All Titles (1996-Current)
subjects Californium - analysis
Computer Simulation
Monte Carlo Method
Neural Networks, Computer
Neutrons
Radiation Dosage
Scintillation Counting - instrumentation
Scintillation Counting - methods
title Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301
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