[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121617-en":3,"doc-seo-121617-105":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},121617,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Constraining cosmology with machine learning and galaxy clustering - the CAMELS-SAM suite","As the next generation of large galaxy surveys comes online, developing and validating machine learning tools for big astronomical data becomes essential. This work introduces CAMELS-SAM, a public suite of one thousand dark-matter-only simulations with broad astrophysical variation using the Santa Cruz semianalytic model. Neural networks are trained on clustering summary statistics to marginalize over astrophysics and constrain cosmology across nonlinear and linear scales (0:68 \u003C R \u003C 27 h−1 cMpc), yielding 3–8% errors on Ωm and σ8, while also testing impacts of galaxy selection, sampling, and statistic choice.","arXiv :2204 .02408v1 [ astro-ph .GA] 5 Apr 2022  \nDRAFT VERSION APRIL 7, 2022  \nTypeset using LATEX twocolumn style in AASTeX631  \nConstraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite  \nLUCIA A. PEREZ  , 1 SHY GENEL  ,2, 3 FRANCISCO VILLAESCUSA-NAVARRO  ,2, 4 RACHEL S. SOMERVILLE ,2 AUSTEN GABRIELPILLAI  ,5, 6, 7 DANIEL ANGLS-ALCZAR  , 8, 2 BENJAMIN D. WANDELT  ,9, 2 AND L. Y. AARON YUNG 6  \n1 School of Earth and Space Exploration, Arizona State University, 781 Terrace Mall, Tempe, AZ 85287, USA  \n2 Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York, NY 10010, USA  \n3 Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027, USA  \n4 Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton NJ 08544, USA  \n5 Institute for Astrophysics and Computational Sciences, Catholic University of America, USA  \n6 Astrophysics Science Division, NASA GSFC, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA  \n7 Center for Research and Exploration in Space Science and Technology, NASA GSFC, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA  \n8 Department of Physics, University of Connecticut, 196 Auditorium Road, Storrs, CT 06269, USA  \n9 Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS, Sorbonne Universit e´, France  \nSubmitted to ApJ  \nABSTRACT  \nAs the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative datasets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 h􀀀1 cMpc)3 with different cosmological parameters (􀀊m and 􀀛8 ) and run through the Santa Cruz semianalytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across 0:68 \u003C R \u003C 27 h 􀀀1 cMpc. Our cosmological constraints cluster around 3-8% error on 􀀊M and 􀀛8 , and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: [https://camels-sam.readthedocs.io](https://camels-sam.readthedocs.io).  \nKeywords: large scale structure, machine learning, cosmology, simulations  \n1. INTRODUCTION  \nSince the earliest galaxy redshift surveys, it has been known that galaxies are not distributed randomly in space, but trace out vast structures, including walls, filaments, and voids. Dark matter (DM) makes up the majority of the mass content of the Universe, and is the dominant driver behind large-scale structure formation. The distribution of galaxies in space is heavily influenced by the clustering of dark matter halos, but also carries signatures of how galaxy prop  \nCorresponding author: Lucia A. Perez  \n[lucia.perez.phd@gmail.com](lucia.perez.phd@gmail.com)  \nerties map to the properties of these dark matter halos (Peebles 1980 ; Wechsler & Tinker 2018) . Galaxy clustering is a potential key probe of cosmology, yet accurately describing the baryonic physics that drives galaxy evolution, and determines","cbCaidkch47S5brI","https://ap.wps.com/l/cbCaidkch47S5brI","pdf",2764898,1,40,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is CAMELS-SAM and how was it generated?\",\"answer\":\"CAMELS-SAM is a suite of one thousand dark-matter-only simulations covering different cosmological parameters and run through the Santa Cruz semianalytic model for galaxy formation.\"},{\"question\":\"Which clustering statistics are used to extract cosmological constraints?\",\"answer\":\"The analysis uses the two-point correlation function, count-in-cells, and the Void Probability Function to probe both nonlinear and linear scales.\"},{\"question\":\"What accuracy is achieved for cosmological parameters and what factors are tested?\",\"answer\":\"The resulting cosmological constraints cluster around 3–8% error on Ωm and σ8, and the study examines how galaxy selections, sampling, and the choice of clustering statistics affect the constraints.\"}]","Constraining cosmology with machine learning and galaxy clustering - the CAMELS-SAM suite | PDF",1785805675,101,{"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},"constraining-cosmology-with-machine-learning-and-galaxy-clustering-the-camels-sam-suite","",{"@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/constraining-cosmology-with-machine-learning-and-galaxy-clustering-the-camels-sam-suite/121617/",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","2026-08-04",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 is CAMELS-SAM and how was it generated?","Question",{"text":75,"@type":76},"CAMELS-SAM is a suite of one thousand dark-matter-only simulations covering different cosmological parameters and run through the Santa Cruz semianalytic model for galaxy formation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which clustering statistics are used to extract cosmological constraints?",{"text":80,"@type":76},"The analysis uses the two-point correlation function, count-in-cells, and the Void Probability Function to probe both nonlinear and linear scales.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy is achieved for cosmological parameters and what factors are tested?",{"text":84,"@type":76},"The resulting cosmological constraints cluster around 3–8% error on Ωm and σ8, and the study examines how galaxy selections, sampling, and the choice of clustering statistics affect the constraints.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"slug":118},7,"Healthcare","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":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"]