[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125818-en":3,"doc-seo-125818-105":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},125818,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning","WEAVE is a massively multiplexing spectrograph capable of collecting about one thousand spectra in a single 3 square degree pointing, enabling the WEAVE-StePS survey to target high-S/N observations for roughly 25,000 magnitude-limited galaxies over the next five years. For star-forming systems (log sSFR ≳ −11), the method achieves a bias near 0.01 dex and dispersion near 0.10 dex, while quiescent galaxies (log sSFR ≲ −11) show larger bias (0.61–0.86 dex) and dispersion (~0.4 dex) depending on noise and redshift. Random forest models outperform k-nearest neighbours, and green-valley classification remains effective across redshift and S/N. Machine learning estimates physical parameters accurately even at S/N,obs ≈ 10 per Å with ancillary photometry, with Bayesian inference producing comparable results. Training enables substantially lower computation time once models are built.","University of Birmingham  \nRetrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning  \nAngthopo, J. ; Granett, B. R. ; Barbera, F. La; Longhetti, M. ; Iovino, A. ; Fossati, M. ; Ditrani, F. R. ; Costantin, L. ; Zibetti, S. ; Gallazzi, A. ; Sánchez-Blázquez, P. ; Tortora, C. ; Spiniello, C. ; Poggianti, B. ; Vazdekis, A. ; Balcells, M. ; Bardelli, S. ; Benn, C. R. ; Bianconi, M. ; Bolzonella, M. DOI:  \n10.48550/arXiv.2406.11748  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nOther version  \nCitation for published version (Harvard):  \nAngthopo, J, Granett, BR, Barbera, FL, Longhetti, M, Iovino, A, Fossati, M, Ditrani, FR, Costantin, L, Zibetti, S, Gallazzi, A, Sánchez-Blázquez, P, Tortora, C, Spiniello, C, Poggianti, B, Vazdekis, A, Balcells, M, Bardelli, S, Benn, CR, Bianconi, M, Bolzonella, M, Busarello, G, Cassarà, LP, Corsini, EM, Cucciati, O, Dalton, G, FerréMateu, A, García-Benito, R, Delgado, RMG, Gafton, E, Gullieuszik, M, Haines, CP, Iodice, E, Ikhsanova, A, Jin, S, Knapen, JH, McGee, S, Mercurio, A, Merluzzi, P, Morelli, L, Moretti, A, Murphy, DNA, Pizzella, A, Pozzetti, L, Ragusa, R, Trager, SC, Vergani, D, Vulcani, B, Talia, M & Zucca, E 2024 'Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning' arXiv.  \n[https://doi.org/10.48550/arXiv.2406.11748](https://doi.org/10.48550/arXiv.2406.11748)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \n17 Jun 2024  \nAstronomy & Astrophysics manuscript no. ML_WStePS_JA ©ESO 2024  \nJune 18, 2024  \nRetrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning  \nJ. Angthopo 1 , B.R. Granett 1 , F. La Barbera2 , M. Longhetti 1 , A. Iovino 1 , M. Fossati3 , F.R. Ditrani 1, 3 , L. Costantin4 , S. Zibetti5 , A. Gallazzi5 , P. Sánchez-Blázquez6, 7 , C. Tortora2 , C. Spiniello8, 2 , B. Poggianti9 , A. Vazdekis 10, 11 , M. Balcells 10, 11, 12 , S. Bardelli 13 , C. R. Benn 12 , M. Bianconi 14 , M. Bolzonella 13 , G. Busarello2 , L. P. Cassarà 15 , E. M. Corsini 16, 9 , O. Cucciati 13 , G. Dalton8, 17 , A. Ferré-Mateu 10, 11 , R. García-Benito 18 , R.M. González Delgado 18 , E. Gafton 12 , M. Gullieuszik9 , C. P. Haines 19, 1 , E. Iodice2 , A. Ikhsanova 16 , S. Jin8, 20 , J. H. Knapen 10, 11 , S. McGee 14 , A. Mercurio21, 2 , P. Merluzzi2 , L. Morelli 19, 1 , A. Moretti9 , D.N.A. Murphy22 , A. Pizzella 16, 9 , L. Pozzetti 13 , R. Ragusa2 , S.  \nC. Trager20 , D. Vergani 13 , B. Vulcani9 , M.","cbCaii0IAMxF6x2E","https://ap.wps.com/l/cbCaii0IAMxF6x2E","pdf",1455214,1,20,"English","en",105,"# Abstract\n## Key words\n# 1. Introduction","[{\"question\":\"What survey and instrument motivate this study?\",\"answer\":\"The work targets the WEAVE/WEAVE-StePS programme, using the William Herschel Telescope Enhanced Area Velocity Explorer to obtain large numbers of spectra efficiently.\"},{\"question\":\"How do the results differ between star-forming and quiescent galaxies?\",\"answer\":\"For star-forming galaxies (log sSFR ≳ −11) the bias is ~0.01 dex with dispersion ~0.10 dex, while quiescent galaxies (log sSFR ≲ −11) show higher bias (0.61–0.86 dex) and dispersion around ~0.4 dex depending on noise and redshift.\"},{\"question\":\"Which machine-learning algorithm performs best for parameter retrieval?\",\"answer\":\"The random forest algorithm outperforms the K-nearest neighbours approach.\"}]","Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning | PDF",1785901391,50,{"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},"retrieval-of-the-physical-parameters-of-galaxies-from-weave-steps-like-data-using-machine-learning-125818","",{"@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/retrieval-of-the-physical-parameters-of-galaxies-from-weave-steps-like-data-using-machine-learning-125818/125818/",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},"What survey and instrument motivate this study?","Question",{"text":75,"@type":76},"The work targets the WEAVE/WEAVE-StePS programme, using the William Herschel Telescope Enhanced Area Velocity Explorer to obtain large numbers of spectra efficiently.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the results differ between star-forming and quiescent galaxies?",{"text":80,"@type":76},"For star-forming galaxies (log sSFR ≳ −11) the bias is ~0.01 dex with dispersion ~0.10 dex, while quiescent galaxies (log sSFR ≲ −11) show higher bias (0.61–0.86 dex) and dispersion around ~0.4 dex depending on noise and redshift.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning algorithm performs best for parameter retrieval?",{"text":84,"@type":76},"The random forest algorithm outperforms the K-nearest neighbours approach.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]