[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122690-en":3,"doc-seo-122690-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122690,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Revisiting mass-radius relationships for exoplanet populations - a machine learning insight","The growing number of exoplanet discoveries and advances in machine learning techniques enable new ways to study the properties of worlds beyond our Solar System. This work applies efficient unsupervised machine learning to a dataset of 762 confirmed exoplanets plus eight Solar System planets to classify bodies into “small” and “giant” groups. It then uses multiple regression models and shows that planetary mass, orbital period, and stellar mass strongly drive exoplanet radius prediction. Support Vector Regression performs best, and parametric equations are derived.","arXiv :2301 .07143v2 [ astro-ph .EP] 17 Aug 2023  \nRevisiting mass-radius relationships for exoplanet populations: a machine learning insight  \nM. Mousavi-Sadr, 1★ D. M. Jassur, 1 and G. Gozaliasl2,3†  \n1 Department of Theoretical Physics and Astrophysics, Faculty of Physics, University of Tabriz, Tabriz, Iran  \n2 Department of Computer Science, Aalto University, P. O. Box 15400, Espoo, FI-00076, Finland  \n3 Department of Physics, University of Helsinki, P. O. Box 64, FI-00014, Helsinki, Finland  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nThe growing number of exoplanet discoveries and advances in machine learning techniques have opened new avenues for exploring and understanding the characteristics of worlds beyond our Solar System. In this study, we employ efficient machine learning approaches to analyze a dataset comprising 762 confirmed exoplanets and eight Solar System planets, aiming to characterize their fundamental quantities. By applying different unsupervised clustering algorithms, we classify the data into two main classes: “small” and “giant” planets, with cut-off values at 􀀧􀀿 = 8. 13􀀧 ⊕ and 􀀢􀀿 = 52.48􀀢⊕ . This classification reveals an intriguing distinction: giant planets have lower densities, suggesting higher H-He mass fractions, while small planets are denser, composed mainly of heavier elements. We apply various regression models to uncover correlations between physical parameters and their predictive power for exoplanet radius. Our analysis highlights that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius. Among the models evaluated, the Support Vector Regression consistently outperforms others, demonstrating its promise for obtaining accurate planetary radius estimates. Furthermore, we derive parametric equations using the M5P and Markov Chain Monte Carlo methods. Notably, our study reveals a noteworthy result: small planets exhibit a positive linear mass-radius relation, aligning with previous findings. Conversely, for giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.  \nKey words: planets and satellites: detection-planets and satellites: dynamical evolution and stability-planets and satellites: formation-planets and satellites: general.  \n1 INTRODUCTION  \nOur comprehension of new worlds beyond the Solar System, known as exoplanets, their population, and diversity come largely from the latest generation of modern satellites. The Kepler space mission, the Transiting Exoplanet Survey Satellite, the James Webb Space Telescope, and many ground-based observatories make important contributions to detecting and characterizing exoplanets (Pepper et al. 2007; Borucki et al. 2010; Beichman et al. 2014) . The data generated by these state-of-the-art instruments are now available to everyone. Researchers skilled in data science, data analytics, or machine learning (ML) and neural network techniques study and analyze these data to predict, identify, characterize, and classify the exoplanets (Alibert & Venturini 2019; MacDonald 2019; Barboza et al. 2020; Tasker et al. 2020; Armstrong et al. 2021; Leleu et al. 2021a,b; MousaviSadr et al. 2021; Schlecker et al. 2021; Van Eylen et al. 2021; Mishra et al. 2023b; Maltagliati 2023) . In addition, the observational data are not only used to study exoplanets but they are also applied to peruse entire planetary science. As many planets are found around  \n★ [E-mail: mahdiyar.mousavi@gmail.com](E-mail: mahdiyar.mousavi@gmail.com)  \n† [E-mail: ghassem.gozaliasl@helsinki.fi](E-mail: ghassem.gozaliasl@helsinki.fi)  \nother stars, they have provided us with an opportunity to understand the main ways of planet formation and evolution and to put our Solar System in a broader context (Kipping 2018; Armitage 2020; Gilbert & Fabrycky 2020; Mishra et","cbCaitOWRreDsl0h","https://ap.wps.com/l/cbCaitOWRreDsl0h","pdf",16946324,1,17,"English","en",105,"# Abstract\n# 1 Introduction","[{\"question\":\"How does the study classify exoplanets into small and giant populations?\",\"answer\":\"It applies unsupervised clustering algorithms to a dataset of confirmed exoplanets (plus Solar System planets) and separates the data into two main classes using specified cutoff values.\"},{\"question\":\"Which factors most influence predicting exoplanet radius in the regression analysis?\",\"answer\":\"The analysis shows that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius.\"},{\"question\":\"What is the best-performing regression model for radius estimation?\",\"answer\":\"Support Vector Regression consistently outperforms the other evaluated models, indicating strong predictive capability for estimating planetary radius.\"}]","Revisiting mass-radius relationships for exoplanet populations - a machine learning insight | PDF",1785812252,43,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"revisiting-mass-radius-relationships-for-exoplanet-populations-a-machine-learning-insight","",{"@graph":36,"@context":86},[37,54,69],{"@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/revisiting-mass-radius-relationships-for-exoplanet-populations-a-machine-learning-insight/122690/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the study classify exoplanets into small and giant populations?","Question",{"text":76,"@type":77},"It applies unsupervised clustering algorithms to a dataset of confirmed exoplanets (plus Solar System planets) and separates the data into two main classes using specified cutoff values.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which factors most influence predicting exoplanet radius in the regression analysis?",{"text":81,"@type":77},"The analysis shows that planetary mass, orbital period, and stellar mass play crucial roles in predicting exoplanet radius.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the best-performing regression model for radius estimation?",{"text":85,"@type":77},"Support Vector Regression consistently outperforms the other evaluated models, indicating strong predictive capability for estimating planetary radius.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]