[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125004-en":3,"doc-seo-125004-105":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},125004,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning","WEAVE is a new massively multiplexing spectrograph enabling ~1000 spectra per 3 deg² field in a single observation. The WEAVE-StePS project will use it to obtain high-S/N, magnitude-limited (IAB=20.5) spectra for ~25,000 galaxies. For star-forming systems (log sSFR≳−11) the bias is ~0.01 dex with ~0.10 dex dispersion, while quiescent galaxies (log sSFR≲−11) show higher bias 0.61–0.86 dex and ~0.4 dex dispersion. Random forests outperform k-nearest neighbours, and green-valley classification remains successful across redshifts and S/N. Machine learning recovers physical parameters from simulated WEAVE-StePS-like spectra even at low S/Nobs=10 per Å, using available photometric auxiliaries; Bayesian inference yields comparable results with less speed for ML.","17 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. Talia23, 13 , and E. Zucca 13  \n(Affiliations can be found after the references)  \nLast edited-June 18, 2024  \nABSTRACT  \nContext. The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼ 25 000 galaxies at  \narXiv :2406 . 11748v1  \nstar-forming galaxies, log sSFR≳ −11, where the bias is ∼ 0.01 dex and the dispersion is ∼ 0. 10 dex. However, for more quiescent galaxies, with log sSFR≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼ 0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns.  \nConclusions. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI,obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.  \nKey words. galaxies: general-galaxies: formation-galaxies: evolution-galaxies: star formation-galaxies: stellar content-galaxies: statistics  \n1. Introduction  \nOver the last two decades, several wide-area photometric and spectroscopic surveys have greatly improved our understanding of galaxy formation and evolution. Most notably, the combination of wide-area and pencil-beam spectroscopic surveys, including the Sloan Digital Sky Survey (SDSS; York et al. 2000), the Galaxy And Mass Assembly (GAMA; Hopkins et al. 2013), zCOSMOS (Lilly et al. 2009), the VIMOS Public Extragalactic Redshift Survey (VIPERS; Guzzo et al. 2014), and 3DHST (Momcheva et al. 2016), have pushed the boundaries of galaxy formation studies to a few billion years after the Big Bang. These spectroscopic efforts usually target well-known fields, where multi-wavelength imaging campaigns provide deep complementary datasets, often covering the UV to near-infrared (NIR) parts of the electromagnetic spectrum. These surveys in-  \nclude the SDSS imaging survey, the Cosmic Assembly Nearinfrared Deep Extragalactic Legacy Survey (CANDELS; Koekemoer et al. 2011), UltraVISTA (McCracken et al. 2012), and the Hyper Suprime-Cam Subaru Strategic Prime (HSC-SSP; Aihara et al. 2018) . Exploitation of these data in combination with numerical hydrodynamic simulations (Crain et al. 2015; Schaye et al. 2015; Springel et al. 2","cbCaiaNmYd1VeiiL","https://ap.wps.com/l/cbCaiaNmYd1VeiiL","pdf",1500919,1,19,"English","en",105,"# Abstract\n## Conclusions\n# 1. Introduction","[{\"question\":\"What does the WEAVE-StePS survey aim to achieve with its instrument?\",\"answer\":\"It uses WEAVE to collect high-S/N spectra for a magnitude-limited sample of about 25,000 galaxies, enabling detailed estimation of galaxy physical parameters.\"},{\"question\":\"How do the machine-learning results differ between star-forming and quiescent galaxies?\",\"answer\":\"For star-forming galaxies (log sSFR≳−11) the bias is ~0.01 dex with ~0.10 dex dispersion, whereas quiescent galaxies (log sSFR≲−11) show a larger bias (0.61–0.86 dex) and higher dispersion (~0.4 dex) depending on noise and redshift.\"},{\"question\":\"Which machine-learning method performs best in the study, and why is it useful?\",\"answer\":\"The random forest algorithm outperforms k-nearest neighbours. Once trained, the machine-learning approach requires substantially less time than other methods while still producing accurate parameter estimates.\"}]","Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning | PDF",1785896037,48,{"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","",{"@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/125004/",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 does the WEAVE-StePS survey aim to achieve with its instrument?","Question",{"text":75,"@type":76},"It uses WEAVE to collect high-S/N spectra for a magnitude-limited sample of about 25,000 galaxies, enabling detailed estimation of galaxy physical parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the machine-learning 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 ~0.10 dex dispersion, whereas quiescent galaxies (log sSFR≲−11) show a larger bias (0.61–0.86 dex) and higher dispersion (~0.4 dex) depending on noise and redshift.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning method performs best in the study, and why is it useful?",{"text":84,"@type":76},"The random forest algorithm outperforms k-nearest neighbours. Once trained, the machine-learning approach requires substantially less time than other methods while still producing accurate parameter estimates.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]