[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124947-en":3,"doc-seo-124947-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124947,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine learning meets Kepler - inverting Kepler’s equation for All vs All conjunction analysis","The number of satellites in orbit around Earth is increasing rapidly, raising collision risk and requiring reliable analysis of satellite population trends and proposed rules. Large-scale simulations must propagate satellite orbits over long timescales to generate actionable close encounters (conjunctions). Rigorous conjunction checking by predicting future orbital states is computationally expensive for many objects, motivating conjunction filters to remove non-conjuncting pairs. This work investigates machine-learning-based conjunction filters using algorithms such as XGBoost, TabNet, and physics-informed neural networks.","Machine learning meets Kepler  \nCitation for published version (APA):  \nOtto, K. , Burgis, S. , Kersting, K. , Bertrand, R. , & Dhami, D. S. (2024) . Machine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis. Machine Learning: Science and Technology , 5(2), Article 025069. [https://doi.org/10.1088/2632-2153/ad51cc](https://doi.org/10.1088/2632-2153/ad51cc)  \nDocument license:  \nCC BY  \nDOI:  \n10.1088/2632-2153/ad51cc  \nDocument status and date:  \nPublished: 01/06/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 04. Aug. 2026  \nPAPER • OPEN ACCESS  \nMachine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis  \nTo cite this article: Kevin Otto et al 2024 Mach. Learn. : Sci. Technol. 5 025069  \nView the article online for updates and enhancements.  \nYou may also like  \n-Time and Spatially Resolved Study of Fuel Cell Reactions Using In situ X-ray Absorption Spectroscopy  \nDitty Dixon, Julia Melke, Sebastian Kaserer et al.  \n-Turbulent Poiseuille Flow with Wall Transpiration: Analytical Study and Direct Numerical Simulation  \nV S Avsarkisov, M Oberlack and G Khujadze  \n-Kaon and antikaon production in heavy ion collisions at 1.5 A GeV  \nAndreas Förster,(for the KaoS Collaboration):, I Böttcher et al.  \nThis content was downloaded from IP address [131.155.153.151](131.155.153.151) on 06/03/2025 at 12:11  \n Mach. Learn.: Sci. Technol. 5 (2024) 025069 [https://doi.org/10.1088/2632-2153/ad51cc](https://doi.org/10.1088/2632-2153/ad51cc)  \nOPEN ACCESS  \nRECEIVED  \n2 January 2024  \nREVISED  \n18 April 2024  \nACCEPTED FOR PUBLICATION  \n29 May 2024  \nPUBLISHED  \n14 June 2024  \nOriginal Content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPAPER  \nMachine learning meets Kepler: inverting Kepler’s equation for All vs All conjunction analysis  \nKevin Otto1, ∗􀁂, Simon Burgis2, Kristian Kersting1,3,4,7, Reinhold Bertrand2,5 and Devendra Singh Dhami3,6  \n1 Computer Science Department, Technical University of Darmstadt, Darmsta","cbCairxH2G7nNXu4","https://ap.wps.com/l/cbCairxH2G7nNXu4","pdf",2019972,1,22,"English","en",105,"# Abstract\n## Motivation: satellite conjunction risk\n## Challenge: computationally expensive filtering\n## Approach: ML-based conjunction filters\n## Methods: XGBoost, TabNet, physics-informed neural networks, deep operator networks","[{\"question\":\"Why are conjunction filters needed in satellite collision risk analysis?\",\"answer\":\"Checking conjunctions by computing future orbital states is computationally expensive when many objects are involved. Conjunction filters remove non-conjuncting orbit pairs from the candidate list to reduce cost.\"},{\"question\":\"What problem does the paper address with machine learning?\",\"answer\":\"The paper explores machine-learning-based conjunction filters to improve the identification of potentially colliding satellite pairs while avoiding the high computational burden of rigorous checking.\"},{\"question\":\"Which machine learning methods are investigated for conjunction filtering?\",\"answer\":\"The work evaluates algorithms including eXtreme Gradient Boosting (XGBoost), TabNet, physics-informed neural networks, and deep operator networks.\"}]","Machine learning meets Kepler - inverting Kepler’s equation for All vs All conjunction analysis | PDF",1785895534,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-meets-kepler-inverting-keplers-equation-for-all-vs-all-conjunction-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-meets-kepler-inverting-keplers-equation-for-all-vs-all-conjunction-analysis/124947/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are conjunction filters needed in satellite collision risk analysis?","Question",{"text":75,"@type":76},"Checking conjunctions by computing future orbital states is computationally expensive when many objects are involved. Conjunction filters remove non-conjuncting orbit pairs from the candidate list to reduce cost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper address with machine learning?",{"text":80,"@type":76},"The paper explores machine-learning-based conjunction filters to improve the identification of potentially colliding satellite pairs while avoiding the high computational burden of rigorous checking.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are investigated for conjunction filtering?",{"text":84,"@type":76},"The work evaluates algorithms including eXtreme Gradient Boosting (XGBoost), TabNet, physics-informed neural networks, and deep operator networks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]