[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127792-en":3,"doc-seo-127792-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127792,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Estimating the Mass of Galactic Components Using Machine Learning Algorithms","Estimating the masses of galactic components relies on approaches that often require strong assumptions about baryon dynamics or dark-matter models. This study presents an alternative pipeline that predicts disk, bulge, stellar, and total mass using k-nearest neighbours, linear regression, random forest, and neural network algorithms, reducing dependence on any single hypothesis. ugriz photometry serves as input features, trained on spiral galaxies from Guo’s mock catalogue. Results show accurate predictions within the training domain, with neural networks performing best; SDSS validation confirms high-confidence mass estimates and uncovers component–magnitude scaling relations while highlighting biases for less luminous galaxies due to observational limits.","universe   \nArticle  \nEstimating the Mass of Galactic Components Using Machine Learning Algorithms  \nJessica N. López-Sánchez 1,2, *, Erick Munive-Villa 1,2, Ana A. Avilez-López 1 and Oscar M. Martínez-Bravo 1  \n[ astro-ph .GA] 26 Mar 2025  \narXiv :2403 .06178v2  \nCitation: López-Sánchez, J.N.; Munive-Villa, E.; Avilez-López,  \nA.A.; Martínez-Bravo, O.M. Estimating the Mass of Galactic Components Using Machine Learning Algorithms. Universe 2024, 10, 220. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)universe10050220  \nAcademic Editor: Stephen J. Curran  \nReceived: 10 March 2024  \nRevised: 3 April 2024  \nAccepted: 4 May 2024  \nPublished: 15 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 CEICO—FZU, Institute of Physics of the Czech Academy of Sciences, Na Slovance 1999/2,  \n182 00 Prague, Czech Republic; [munive@fzu.cz](munive@fzu.cz) (E.M.-V.); [aavilez@fcfm.buap.mx](aavilez@fcfm.buap.mx) (A.A.A.-L.); [omartin@fcfm.buap.mx](omartin@fcfm.buap.mx) (O.M.M.-B.)  \n2 Facultad de Ciencias Físico-Matemáticas, Ciudad Universitaria, Benemérita Universidad Autónoma de Puebla, Av. San Claudio SN, Col. San Manuel, Puebla 72592, Mexico  \n* Correspondence: [lopez@fzu.cz](lopez@fzu.cz)  \nAbstract: The estimation of galactic component masses can be carried out through various approaches that involve a host of assumptions about baryon dynamics or the dark matter model. In contrast, this work introduces an alternative method for predicting the masses of the disk, bulge, stellar, and total mass using the k-nearest neighbours, linear regression, random forest, and neural network (NN) algorithms, reducing the dependence on any particular hypothesis. The ugriz photometric system was selected as the set of input features, and the training was performed using spiral galaxies in Guo’s mock catalogue from the Millennium simulation. In general, all of the algorithms provide good predictions for the galaxy’s mass from 109 M⊙ to 1011 M⊙, corresponding to the central region of the training domain. The NN algorithm showed the best performance. To validate the algorithm, we used the SDSS survey and found that the predictions of disk-dominant galaxies’ masses lie within a 99% confidence level, while galaxies with larger bulges are predicted at a 95% confidence level. The NN also reveals scaling relations between mass components and magnitudes. However, predictions for less luminous galaxies are biased due to observational limitations. Our study demonstrates the efficacy of these methods with the potential for further enhancement through the addition of observational data or galactic dynamics.  \nKeywords: galactic systems; neural network; scaling relations  \n1. Introduction  \nThe bulge–disk decomposition of galactic systems is useful for understanding the evolutionary processes of galaxies. Specifically, the disk and bulge masses can be inferred, given that their stellar population has different dynamic or even chemical features. There are plenty of schemes for classifying galaxies; one of the most popular corresponds to the morphological classification proposed by Edwin Hubble [1], which distinguishes four different types of galaxies: elliptical, spiral, barred spiral, and irregular. Another method involves the isophotal radius measurement [2], determining the size attributed to a galaxy component according to the corresponding surface brightness level. A way to characterise the light distribution independent of the light profile is through the concentration measure, defined by the ratio of two geometrical regions, each containing a fixed fraction of the total luminosity of the galactic system [3] .  \nAnoth","cbCail4YnHpqe1rF","https://ap.wps.com/l/cbCail4YnHpqe1rF","pdf",2631184,3,1,15,"English","en",105,"# Abstract\n# Introduction\n## Bulge–disk decomposition and galaxy classification\n## Empirical fitting functions for component mass\n## Photometric and spectroscopic decomposition\n## Simulations and semi-analytical models","[{\"question\":\"Which machine learning methods are used to estimate galactic component masses?\",\"answer\":\"The study uses k-nearest neighbours, linear regression, random forest, and neural network (NN) algorithms to predict disk, bulge, stellar, and total masses.\"},{\"question\":\"What input data and training set are employed?\",\"answer\":\"The ugriz photometric system is used as the input feature set, and training is performed using spiral galaxies from Guo’s mock catalogue derived from the Millennium simulation.\"},{\"question\":\"How is the model validated, and what confidence levels are reported?\",\"answer\":\"Validation uses the SDSS survey, finding disk-dominant galaxies’ masses within a 99% confidence level and galaxies with larger bulges at a 95% confidence level.\"}]","Estimating the Mass of Galactic Components Using Machine Learning Algorithms | PDF",1785941711,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"estimating-the-mass-of-galactic-components-using-machine-learning-algorithms","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/estimating-the-mass-of-galactic-components-using-machine-learning-algorithms/127792/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",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},"Which machine learning methods are used to estimate galactic component masses?","Question",{"text":76,"@type":77},"The study uses k-nearest neighbours, linear regression, random forest, and neural network (NN) algorithms to predict disk, bulge, stellar, and total masses.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What input data and training set are employed?",{"text":81,"@type":77},"The ugriz photometric system is used as the input feature set, and training is performed using spiral galaxies from Guo’s mock catalogue derived from the Millennium simulation.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the model validated, and what confidence levels are reported?",{"text":85,"@type":77},"Validation uses the SDSS survey, finding disk-dominant galaxies’ masses within a 99% confidence level and galaxies with larger bulges at a 95% confidence level.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]