Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation

Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation Matthew Herald, Andrei Nicușan, Tzany Kokalova Wheldon, Christopher Windows-Yule, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1846231/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal-processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal-processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose using evolutionary algorithms to perform digitizer calibrations. We demonstrate this by calibrating a digitizer for the Phillips/ACAC Forte, which contains six free parameters. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8\% mean difference to the experiment, improving from 18.3\% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 21 Sep, 2022 Reviews received at journal 15 Sep, 2022 Reviewers agreed at journal 05 Sep, 2022 Reviews received at journal 06 Aug, 2022 Reviewers agreed at journal 02 Aug, 2022 Reviewers invited by journal 02 Aug, 2022 Editor assigned by journal 28 Jul, 2022 Editor invited by journal 14 Jul, 2022 Submission checks completed at journal 14 Jul, 2022 First submitted to journal 11 Jul, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1846231","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":121073081,"identity":"1b007d6a-ffbf-42f6-8b8f-ca4aa192f646","order_by":0,"name":"Matthew Herald","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYFACHoYDCRU2cgYSCAGCWhgPfDiTZkySFuaDM9sOJ24gWovujNwDh3nbmNO3Szc/3cBQY8dgcOYAfi1mZ84lHOY5x5a7c84xsxsMx5IZDM42ENByvMfgME8ZT+6GGwlALWwHGAzOE3CY2WEeoBY2iXSDG+nfbjD8I0YL0JaDM9oMEgxu5JjdYGw7QITDzpwxAAZyguHOGTllNxL7knkkCXr/Ro7xh4SK//LmEunbbnz4ZifHdyaBgMtQQAIxETkKRsEoGAWjgDAAAJL+TMy1WNwkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Birmingham","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Herald","suffix":""},{"id":121073082,"identity":"1c732b26-1216-4896-a929-009bb38a5e5b","order_by":1,"name":"Andrei Nicușan","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrei","middleName":"","lastName":"Nicușan","suffix":""},{"id":121073083,"identity":"2e2ca729-0614-41c4-8e88-b3676c6d78c8","order_by":2,"name":"Tzany Kokalova Wheldon","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tzany","middleName":"Kokalova","lastName":"Wheldon","suffix":""},{"id":121073084,"identity":"bb1814e0-b9d3-4e9a-80c7-f688605b9f97","order_by":3,"name":"Christopher Windows-Yule","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Windows-Yule","suffix":""},{"id":121073085,"identity":"d46e33cc-3024-4130-9693-b14b86de7a85","order_by":4,"name":"Jonathan Seville","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Seville","suffix":""}],"badges":[],"createdAt":"2022-07-11 10:29:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1846231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1846231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24157099,"identity":"fd9b3f93-6da7-4557-8059-e0b64bb4a184","added_by":"auto","created_at":"2022-07-21 17:26:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1226276,"visible":true,"origin":"","legend":"","description":"","filename":"DigitizerLaTeXEdit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1846231/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autonomous Digitizer Calibration of a Monte Carlo Detector Model through Evolutionary Simulation","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1846231/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1846231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1846231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSimulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal-processing. 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