[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121126-en":3,"doc-seo-121126-105":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":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},121126,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploring galactic properties with machine learning - Predicting star formation, stellar mass, and metallicity from photometric data","Aims. This study applies machine learning to forecast key galaxy properties, including star formation rate, stellar mass, and metallicity, for galaxies spanning redshifts from 0.01 to 0.3. Methods. CatBoost and deep learning architectures are trained using multiband optical and infrared photometric data from SDSS and AllWISE, based on the SDSS MPA-JHU DR8 catalogue. Results. Predictions are produced solely from photometric inputs, with minimized RMSE values for star formation rate, stellar mass, and metallicity. Conclusions. The work supports automated estimation for rapidly growing multi-wavelength astronomy datasets and motivates future model refinements.","Astronomy & Astrophysics manuscript no. galaxy_property ©ESO 2024  \nMay 27, 2024  \n-ph .GA] 24 May 2024  \nExploring galactic properties with machine learning Predicting star formation, stellar mass, and metallicity from photometric data  \nF. Z. Zeraatgari 1 , F. Hafezianzadeh2 , Y.-X. Zhang3 , A. Mosallanezhad 1 , and J.-Y. Zhang3  \n1 School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China  \ne-mail: [fzeraatgari@xjtu.edu.cn](fzeraatgari@xjtu.edu.cn); [e-mail:](e-mail: mosallanezhad@xjtu.edu.cn)[ mosallanezhad@xjtu.edu.cn](e-mail: mosallanezhad@xjtu.edu.cn)  \n2 Department of physics, Institute for Advanced Studies in Basic Sciences, Zanjan, 45195-1159, Iran  \n3 CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Beijing, 100101, China e-mail: E-mail: [zyx@bao.ac.cn](zyx@bao.ac.cn)  \nABSTRACT  \nAims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3 .  \nMethods. Leveraging CatBoost and deep learning architectures, we utilize multiband optical and infrared photometric data from SDSS and AllWISE, trained on the SDSS MPA-JHU DR8 catalogue.  \nResults. Our study demonstrates the potential of machine learning in accurately predicting galaxy properties solely from photometric data. We achieve minimised root mean square errors, specifically employing the CatBoost model. For star formation rate prediction, we attain a value of RMSESFR = 0.336 dex, while for stellar mass prediction, the error is reduced to RMSESM = 0.206 dex. Additionally, our model yields a metallicity prediction of RMSEmetallicity = 0.097 dex.  \nConclusions. These findings underscore the significance of automated methodologies in efficiently estimating critical galaxy properties, amid the exponential growth of multi-wavelength astronomy data. Future research may focus on refining machine learning models and expanding datasets for even more accurate predictions.  \nKey words. method: data analysis – methods: statistical – galaxies: star formation – galaxies: evolution – techniques: photometric –  \nastronomical data bases: miscellaneous – catalogues  \narXiv :2405 . 15566v1  \nThe next generation of extensive multi-wavelength photometric sky surveys, exemplified by missions like the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST; Ivezi et al. 2019), the Euclid Space Telescope (Laureijs et al. 2011), and the China Space Station Telescope (CSST; Zhan 2011), are poised to deliver vast datasets containing critical galaxy properties. These observed properties are not only essential metrics but also provide extremely valuable information about various aspects of galaxy evolution, revealing intriguing correlations among them (Brinchmann et al. 2004 ; Tremonti et al. 2004 ; Baldry et al. 2008 ; Lara-Lopez et al. 2010 ; Mannucci et al. 2010 ; Kravtsovet al. 2018) .  \nThe determination of galaxy properties, such as stellar mass, star formation rate, and metallicity, is a complex process that relies on a variety of observational and analytical methods, including the versatile techniques of spectral energy distribution (SED) fitting (Walcher et al. 2011 ; Conroy 2013) . Astronomers use a combination of techniques to uncover the mysteries of galaxies. They utilize telescopes and instruments sensitive to a wide range of wavelengths, spanning from γ ray, X-ray, ultraviolet, optical bands to infrared and radio bands. These instruments capture diverse emissions from galaxies, revealing the radiance of recently born stars in ultraviolet and optical bands, the infrared signals emitted from dust-covered stellar nurseries, and the faint radiation from old, evolved stars within these cosmic structures. Notably, the near-infrared band, with its longer wavelengths, effec-  \ntively traces the stellar mass linked to the old population, shedding light on the evolution of galaxies (Kennicutt & Evans 2012) .  \nSED ","cbCaim1634rxE0MS","https://ap.wps.com/l/cbCaim1634rxE0MS","pdf",17456922,1,12,"English","en",105,"# Abstract\n# Key Aims and Scope\n# Methods and Data Sources\n## Models: CatBoost and Deep Learning\n## Training Catalogue\n# Results and Prediction Performance\n## Star Formation Rate Errors\n## Stellar Mass Errors\n## Metallicity Errors\n# Conclusions and Future Work\n## Next-Generation Photometric Surveys\n## Motivation for Automated Inference","[{\"question\":\"What galaxy properties are predicted in this study?\",\"answer\":\"The study predicts star formation rate, stellar mass, and metallicity across galaxies within redshift 0.01 to 0.3.\"},{\"question\":\"Which data and catalog are used for training the machine learning models?\",\"answer\":\"It uses multiband optical and infrared photometric data from SDSS and AllWISE, trained on the SDSS MPA-JHU DR8 catalogue.\"},{\"question\":\"How accurate are the predictions using photometric data only?\",\"answer\":\"The CatBoost-based approach yields RMSE values of 0.336 dex for star formation rate, 0.206 dex for stellar mass, and 0.097 dex for metallicity.\"}]","Exploring galactic properties with machine learning - Predicting star formation, stellar mass, and metallicity from photometric data | PDF",1785733880,30,{"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},"exploring-galactic-properties-with-machine-learning-predicting-star-formation-stellar-mass-and-metallicity-from-photometric-data","",{"@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/exploring-galactic-properties-with-machine-learning-predicting-star-formation-stellar-mass-and-metallicity-from-photometric-data/121126/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What galaxy properties are predicted in this study?","Question",{"text":75,"@type":76},"The study predicts star formation rate, stellar mass, and metallicity across galaxies within redshift 0.01 to 0.3.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data and catalog are used for training the machine learning models?",{"text":80,"@type":76},"It uses multiband optical and infrared photometric data from SDSS and AllWISE, trained on the SDSS MPA-JHU DR8 catalogue.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the predictions using photometric data only?",{"text":84,"@type":76},"The CatBoost-based approach yields RMSE values of 0.336 dex for star formation rate, 0.206 dex for stellar mass, and 0.097 dex for metallicity.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]