[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127538-en":3,"doc-seo-127538-105":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},127538,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","FLAMINGO - Calibrating large cosmological hydrodynamical simulations with machine learning","Large-scale structure surveys require quantifying how baryonic effects, including active galactic nucleus feedback and star formation, influence cosmological observables. In hydrodynamical simulations, feedback is generated on unresolved scales, so subgrid models with free parameters are needed. The study calibrates AGN and stellar feedback models for FLAMINGO using Gaussian-process emulators trained on Latin hypercubes of smaller simulations, then fits them to observational data while accounting for biases across multiple resolutions.","arXiv :2306 .05492v1 [ astro-ph .CO] 8 Jun 2023  \nFLAMINGO: Calibrating large cosmological hydrodynamical simulations with machine learning  \nRoi Kugel, 1★ Joop Schaye, 1 Matthieu Schaller,2, 1 John C. Helly,3 Joey Braspenning, 1 Willem Elbers,3  \nCarlos S. Frenk,3 Ian G. McCarthy,4 Juliana Kwan,4 Jaime Salcido,4 Marcel P. van Daalen, 1 Bert Vandenbroucke, 1 Yannick M. Bahé, 1,5 Josh Borrow,3,6 Evgenii Chaikin, 1 Filip Huško,3 Adrian Jenkins,3 Cedric G. Lacey,3 Folkert S. J. Nobels, 1 and Ian Vernon7  \n1 Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, the Netherlands  \n2 Lorentz Institute for Theoretical Physics, Leiden University, PO box 9506, 2300 RA Leiden, the Netherlands  \n3 Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham, DH1 3LE, UK  \n4 Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK  \n5 Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland  \n6 Department of Physics, Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  \n7 Department of Mathematical Sciences, Durham University, Stockton Road, DH1 3LE, Durham, UK  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nTo fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryoniceffects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables. In simulations, feedback processes originate on scales that remain unresolved. Therefore, they need to be sourced via subgrid models that contain free parameters. We use machine learning to calibrate the AGN and stellar feedback models for the FLAMINGO cosmological hydrodynamical simulations. Using Gaussian process emulators trained on Latin hypercubes of 32 smaller-volume simulations, we model how the galaxy stellar mass function and cluster gas fractions change as a function of the subgrid parameters. The emulators are then fit to observational data, allowing for the inclusion of potential observational biases. We apply our method to the three different FLAMINGO resolutions, spanning a factor of 64 in particle mass, recovering the observed relations within the respective resolved mass ranges. We also use the emulators, which link changes in subgrid parameters to changes in observables, to find models that skirt or exceed the observationally allowed range for cluster gas fractions and the stellar mass function. Our method enables us to define model variations in terms of the data that they are calibrated to rather than the values of specific subgrid parameters. This approach is useful, because subgrid parameters are typically not directly linked to particular observables, and predictions for a specific observable are influenced by multiple subgrid parameters.  \nKey words: large-scale structure of Universe – cosmology: theory – methods: numerical – methods: statistical – galaxies: clusters: general – galaxies: formation  \n1 INTRODUCTION  \nThe evolution of the large-scale distribution of matter in the Universe is highly sensitive to the underlying cosmological model. Current probes have given us our concordance cosmological model ΛCDM, which consists of a spatially flat universe, where dark energy and cold dark matter dominate the current energy density (for a review see Frieman et al. 2008) .  \nThe concordance model has been independently validated by a large array of probes. These include the cosmic microwave background (CMB) (e.g. Planck Collaboration et al. 2020), galaxy clustering and gravitational lensing (e.g. Abbott et al. 2022; Heymanset al. 2021), baryon acoustic oscillations (BAO) (e.g. Alam et al. 2021), and more (for a review see Turner 2022) . While all the probes  \n★ [E-mail: kugel@strw.leidenuniv.nl](E-mail: kugel@strw.leidenuniv.nl)  \nb","cbCaifjisXMrdBDn","https://ap.wps.com/l/cbCaifjisXMrdBDn","pdf",1902559,4,1,24,"English","en",105,"# Abstract\n# Introduction\n## Cosmological context and observational tensions\n## Large-scale structure modelling with simulations\n## Role of baryonic effects and subgrid calibration","[{\"question\":\"Why are subgrid models necessary in cosmological hydrodynamical simulations?\",\"answer\":\"Feedback processes originate on scales that remain unresolved in the simulations. Subgrid models with free parameters are therefore used to represent these effects.\"},{\"question\":\"How does the work calibrate the AGN and stellar feedback models for FLAMINGO?\",\"answer\":\"Gaussian process emulators are trained on Latin hypercubes of 32 smaller-volume simulations. The emulators then relate subgrid parameters to observables and are fit to observational data.\"},{\"question\":\"What do the results enable regarding model selection and interpretation?\",\"answer\":\"The method allows defining model variations in terms of the data they are calibrated to, not only in terms of specific subgrid parameter values, since multiple parameters can jointly affect a given observable.\"}]","FLAMINGO - Calibrating large cosmological hydrodynamical simulations with machine learning | PDF",1785939825,60,{"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},"flamingo-calibrating-large-cosmological-hydrodynamical-simulations-with-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/flamingo-calibrating-large-cosmological-hydrodynamical-simulations-with-machine-learning/127538/",{"url":53,"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","2026-08-23","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},"Why are subgrid models necessary in cosmological hydrodynamical simulations?","Question",{"text":76,"@type":77},"Feedback processes originate on scales that remain unresolved in the simulations. Subgrid models with free parameters are therefore used to represent these effects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the work calibrate the AGN and stellar feedback models for FLAMINGO?",{"text":81,"@type":77},"Gaussian process emulators are trained on Latin hypercubes of 32 smaller-volume simulations. The emulators then relate subgrid parameters to observables and are fit to observational data.",{"name":83,"@type":74,"acceptedAnswer":84},"What do the results enable regarding model selection and interpretation?",{"text":85,"@type":77},"The method allows defining model variations in terms of the data they are calibrated to, not only in terms of specific subgrid parameter values, since multiple parameters can jointly affect a given observable.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"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":20,"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":30,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]