A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters

A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters | 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 Research Article A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters Omid Rahimpour, Mohsen Salek, Mehdi Khakian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4492994/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Machine Learning methods have emerged as powerful tools for analyzing stellar clusters, which pose significant challenges. techniques such as DBSCAN and GMM have advanced remarkably in this domain. However, these clustering techniques exhibit imperfections and limitations, highlighting the need for careful data tuning and consideration of data characteristics to ensure meaningful result. The utilization of supervised Machine Learning techniques for membership determination of the stellar clusters, especially open clusters, can lead to more accurate results. However, the absence of dataset for training on an open cluster presents a significant hurdle. To address the problem, we've introduced a novel approach to generate a labeled dataset for training the supervised Machine Learning models. Our approach leverages data from Gaia DR3 Catalog, which provides precise astrometric and photometric measurements for millions stars in Milky Way, to construct a comprehensive dataset. Our findings have significant implications for future astronomical research. By using Supervised machine learning techniques, we can achieve more accurate and efficient membership determination for stellar clusters, which can lead to a better understanding of the formation and evolution of galaxies. Our method not only enhances the accuracy of membership determination but also provides insights into the underlying data characteristics that influence cluster analysis. Gaia Star cluster Stellar characteristics Stellar classification Astronomy data analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4492994","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310557682,"identity":"21d68b74-95ef-47b7-92bb-2148565ac366","order_by":0,"name":"Omid Rahimpour","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Omid","middleName":"","lastName":"Rahimpour","suffix":""},{"id":310557683,"identity":"b79cd69d-b2a5-4763-ad01-1a975947f6f4","order_by":1,"name":"Mohsen Salek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie3PMYuDMBQH8BdcKzeXG+xHSD/PLRc655YuDlZShLiU62qh9L6CtzgnBHQRXIV2qEt3N93O6HKL2vHg8h+SR3g/Xh6AicmfjMUQA3gH0JerHyw2Q9BvkmuCnia65sPLJHBCua8iuH3gQu3r9rJ7ewk70rjJKME5CdYxPLa4JMGrnWQ0Ut3cQ34dJ0D48g6KxCXiFkpSyjpiIT5OnGMVtj0pJEftOaVfcwRKwlGsiSAcbObReI7gsgqWEX6Qk95lkQr63RE5tYtz3Mj64N7IZ5FVdeP59FIoeW/ciY8NswSsRF+p/hQz/UOPw/rCf6LZxMTE5L/lBznSacINsvkoAAAAAElFTkSuQmCC","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Salek","suffix":""},{"id":310557684,"identity":"693376d7-ca4d-4a69-980f-2245d67ade27","order_by":2,"name":"Mehdi Khakian","email":"","orcid":"","institution":"Amirkabir University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Khakian","suffix":""}],"badges":[],"createdAt":"2024-05-28 20:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4492994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4492994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57811565,"identity":"f5a11ec9-c0fd-4e02-bf65-fe923eeaf0aa","added_by":"auto","created_at":"2024-06-06 02:58:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265242,"visible":true,"origin":"","legend":"","description":"","filename":"MembershipDetermination.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4492994/v1_covered_fac53289-67c3-4180-8e1e-c75c02e962a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Preparation Approach for Supervised ML Membership Determination of Open Clusters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gaia, Star cluster, Stellar characteristics, Stellar classification, Astronomy data analysis","lastPublishedDoi":"10.21203/rs.3.rs-4492994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4492994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine Learning methods have emerged as powerful tools for analyzing stellar clusters, which pose significant challenges. techniques such as DBSCAN and GMM have advanced remarkably in this domain. 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