[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124485-en":3,"doc-seo-124485-105":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},124485,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds","Differences in metallicity between the Large Magellanic Cloud and the Small Magellanic Cloud enable testing whether environmental chemical composition changes machine learning performance for classifying dusty stellar sources. Five classes—YSOs, RSGs, PAGBs, and oxygen- and carbon-rich AGB stars—are modeled using spectroscopically labeled SAGE data and a probabilistic random forest. Training variants separate galaxies, exclude the sparse PAGB class, combine datasets, or cross-train between LMC and SMC, yielding comparable accuracies (high 80s–low 90s) and indicating metallicity has little impact. Misclassifications in SMC mainly trace to the very small PAGB sample.","arXiv :2511 . 12210v1 [ astro-ph .GA] 15 Nov 2025  \nMetallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds  \nSepideh Ghaziasgar ∗1, Mahdi Abdollahi †1, Atefeh Javadi ‡1, Jacco Th. van Loon §2, Iain McDonald ¶3, Joana Oliveira ‖2, and Habib G. Khosroshahi ∗∗1,4  \n1 School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19568-36613, Tehran, Iran  \n2 Lennard-Jones Laboratories, Keele University, ST5 5BG, UK  \n3 Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, M13 9PL, UK  \n4 Iranian National Observatory, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran  \nAbstract  \nDifferences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying dusty stellar sources. The five stellar classes studied include young stellar objects (YSOs), red supergiants (RSGs), post-asymptotic giant branch stars (PAGBs), and oxygenand carbon-rich asymptotic giant branch stars (OAGBs and CAGBs), which are key phases of stellar evolution involved in dust production. Using spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, we trained and evaluated a probabilistic random forest (PRF) classifier with four approaches: (1) separate training on LMC and SMC, including all five classes, (2) excluding the underpopulated PAGB class, (3) combined LMC and SMC datasets, and (4) cross-galaxy training and testing. The model achieved 93% accuracy on the SMC and 88% on the LMC across all five classes. In the SMC, PAGB sources were misclassified as YSOs, mainly because of their small sample size (4 objects) . When PAGB was excluded, both the LMC and the SMC reached 92% accuracy. A combined dataset produced the same accuracy, and cross-galaxy training yielded similar results, indicating that metallicity does not significantly impact model performance. A comparison of absolute CMDs for the LMC and SMC confirms their similarity in stellar populations. These findings suggest that environmental metallicity has little effect on ML-based classification of dusty stellar sources, supporting the use of combined datasets and cross-galaxy models in low-metallicity environments.  \nKeywords:stars: classification - stars: AGB, RSG, and post-AGB - stars: YSOs - galaxies: metallicity   \ngalaxies: spectral catalog - galaxies: Local Group - methods: machine learning  \n1 . Introduction  \nThe Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) are nearby dwarf galaxies at distances of approximately 50 and 60 kpc, with metallicities of about 0.5 Z ⊙ and 0.2 Z⊙, respectively (Pietrzy´nski et al. , 2013 , Russell & Dopita, 1992 , Subramanian & Subramaniam, 2009) . Their proximity and contrasting chemical compositions make them excellent laboratories for investigating the relationship between stellar evolution, dust formation, and environmental metallicity (Ruffle et al. , 2015) .  \nDusty stellar sources, including young stellar objects (YSOs) and evolved stars—oxygen- and carbonrich asymptotic giant branch stars (OAGBs, CAGBs), red supergiants (RSGs), and post-asymptotic giant branch stars (PAGBs)—represent key phases in the stellar life cycle. These objects return heavy elements to the interstellar medium, shaping the dust content and chemical enrichment of galaxies (Boyer et al. , 2011 ,  \n∗ [sepideh.ghaziasgar@ipm.ir](sepideh.ghaziasgar@ipm.ir), Corresponding author †[m.abdollahi@ipm.ir](m.abdollahi@ipm.ir)  \n‡[atefeh@ipm.ir](atefeh@ipm.ir)  \n§[j.t.van.loon@keele.ac.uk](j.t.van.loon@keele.ac.uk)  \n¶ [Iain.Mcdonald-2@manchester.ac.uk](Iain.Mcdonald-2@manchester.ac.uk)[ ](Iain.Mcdonald-2@manchester.ac.uk)‖[j.oliveira@keele.ac.uk](j.oliveira@keele.ac.uk)  \n∗∗ [habib@ipm.ir](habib@ipm.ir)  \nGhaziasgar [S. et](S. et) al.  \ndoi:  \n1  \nH¨ofner & Olofsson,","cbCairv2S1YiX8gk","https://ap.wps.com/l/cbCairv2S1YiX8gk","pdf",911979,1,6,"English","en",105,"# Introduction\n## Data and Methods\n## Results and Discussion\n## Conclusion","[{\"question\":\"How does metallicity difference between LMC and SMC relate to the study’s goal?\",\"answer\":\"The study tests whether the metallicity contrast between the LMC and SMC changes the accuracy of machine learning models when classifying dusty stellar sources.\"},{\"question\":\"What data and model were used for the classifications?\",\"answer\":\"Spectroscopically labeled datasets from the SAGE project were used, and a probabilistic random forest (PRF) classifier was trained and evaluated under several training strategies.\"},{\"question\":\"Do the findings suggest metallicity significantly affects model performance?\",\"answer\":\"No. Accuracies remain similar across LMC and SMC under multiple training setups, and comparisons of absolute CMDs support that stellar populations are sufficiently alike for classification.\"}]","Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds | PDF",1785822724,15,{"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},"metallicity-effects-on-machine-learning-classification-of-dusty-stellar-sources-in-the-magellanic-clouds","",{"@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/metallicity-effects-on-machine-learning-classification-of-dusty-stellar-sources-in-the-magellanic-clouds/124485/",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","2026-08-04",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},"How does metallicity difference between LMC and SMC relate to the study’s goal?","Question",{"text":75,"@type":76},"The study tests whether the metallicity contrast between the LMC and SMC changes the accuracy of machine learning models when classifying dusty stellar sources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and model were used for the classifications?",{"text":80,"@type":76},"Spectroscopically labeled datasets from the SAGE project were used, and a probabilistic random forest (PRF) classifier was trained and evaluated under several training strategies.",{"name":82,"@type":73,"acceptedAnswer":83},"Do the findings suggest metallicity significantly affects model performance?",{"text":84,"@type":76},"No. Accuracies remain similar across LMC and SMC under multiple training setups, and comparisons of absolute CMDs support that stellar populations are sufficiently alike for 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