[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123619-en":3,"doc-seo-123619-105":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},123619,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Analysis of Dark Matter Halo Structure Formation in N-body Simulations with Machine Learning - Preprint","The properties of the matter density field in cosmological initial conditions strongly shape the large-scale structure observed today. N-body simulations are essential for analyzing high-density collapsed regions that form dark matter halos. This work trains machine learning models on N-body simulation information to infer halo classification labels and to predict halo formation from initial density characteristics. It also reconstructs the Halo Mass Function, using both theoretical methods and trained algorithms. Random Forest and Neural Networks deliver the best classification and provide model-independent, highly accurate HMF fitting with only limited data points.","arXiv :2303 .09098v2 [ astro-ph .CO] 19 Jun 2023  \nANALYSIS OF DARK MATTER HALO STRUCTURE FORMATION INN-BODY SIMULATIONS WITH MACHINE LEARNING  \nJazhiel Chacón  \nCentro de Investigación en Computación, Instituto Politécnico Nacional,  \n07738, Ciudad de México, México  \nInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México,  \n62210, Cuernavaca, Morelos, México.  \n[chaconl2021@cic.ipn.mx](chaconl2021@cic.ipn.mx)  \nIsidro Gómez-Vargas  \nInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México,  \n62210, Cuernavaca, Morelos, México.  \n[igomez@icf.unam.mx](igomez@icf.unam.mx)  \nRicardo Menchaca Méndez  \nCentro de Investigación en Computación, Instituto Politécnico Nacional,  \n07738, Ciudad de México, México.  \n[ric@cic.ipn.mx](ric@cic.ipn.mx)  \nJ. Alberto Vázquez  \nInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México,  \n62210, Cuernavaca, Morelos, México.  \n[javazquez@icf.unam.mx](javazquez@icf.unam.mx)  \nJune 21, 2023  \nABSTRACT  \nThe properties of the matter density field in the initial conditions have a decisive impact on the features of the large-scale structure of the Universe as observed today. These need to be studied via N-body simulations, which are imperative to analyze high density collapsed regions into dark matter halos. In this paper, we train Machine Learning algorithms with information from N-body simulations to infer two properties: dark matter particle halo classification that leads to halo formation prediction with the characteristics of the matter density field traced back to the initial conditions, and dark matter halo formation by calculating the Halo Mass Function (HMF), which offers the number density of dark matter halos with a given threshold. We map the initial conditions of the density field into classification labels of dark matter halo structures. The Halo Mass Function of the simulations is calculated and reconstructed with theoretical methods as well as our trained algorithms.  \nWe test several Machine Learning techniques where we could find that the Random Forest and Neural Networks proved to be the better performing tools to classify dark matter particles in cosmological simulations. We also show that, by using only a few data points, we can effectively train the algorithms to reconstruct the Halo Mass Function in a model-independent way, giving us a highly accurate fitting function that aligns well with both simulation and theoretical results.  \nKeywords Numerical Simulations, N-body systems, Machine Learning, Neural Networks, Cosmology · Machine Learning  \nA PREPRINT-JUNE 21, 2023  \n1 Introduction  \nBy studying the cosmological structure formation in the standard model, also known as ΛCDM, we are able to determine that the total amount of the Universe is divided among several constitutes: visible matter (baryonic matter), which takes about 4.9 % of the total amount; neutrinos and photons which today are estimated that from less than 0.1% of the total content; dark matter, a hypothetical constituent of the Universe with purely gravitational interaction which collapses into filaments, halos and structures that eventually ended up merging the visible matter that creates galaxies and adds up about 23% of the total content of the Universe and finally dark energy, another hypothetical constituent, but this one being the responsible for the current accelerated expansion of the Universe, with the remaining 72 % of the total content of the Universe [3] .  \nThe presence of dark matter and dark energy, the so called ‘dark sector’ of the Universe, can be inferred from observational evidence of large-scale structures, which can be studied with analytical and semi-analytical models. Nevertheless, only numerical simulations are capable to emulate the small scale structures and sub-structures observed in the Universe, that give rise to a cosmic network of filaments, voids and groups. These structures act as gravitational wells around the visible matter, which event","cbCaigSSGCOu0EZO","https://ap.wps.com/l/cbCaigSSGCOu0EZO","pdf",1011667,1,11,"English","en",105,"# Introduction\n## Cosmological context (ΛCDM and dark sector)\n## Role of numerical simulations and machine learning","[{\"question\":\"How do initial conditions affect dark matter halo structure formation?\",\"answer\":\"The matter density field properties in the initial conditions determine key characteristics of the Universe’s large-scale structure observed today and influence how collapsed regions form halos.\"},{\"question\":\"What two halo-related quantities does the study infer using machine learning?\",\"answer\":\"It infers (1) dark matter halo classification linked to halo formation prediction and (2) dark matter halo formation via reconstruction of the Halo Mass Function (HMF).\"},{\"question\":\"Which machine learning methods performed best, and how efficiently are they trained?\",\"answer\":\"Random Forest and Neural Networks performed best for classifying dark matter particles. The models can also reconstruct the HMF effectively using only a few data points, yielding a highly accurate fitting function aligned with simulations and theory.\"}]","Analysis of Dark Matter Halo Structure Formation in N-body Simulations with Machine Learning - Preprint | PDF",1785817664,28,{"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},"analysis-of-dark-matter-halo-structure-formation-in-n-body-simulations-with-machine-learning-preprint","",{"@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/analysis-of-dark-matter-halo-structure-formation-in-n-body-simulations-with-machine-learning-preprint/123619/",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},"How do initial conditions affect dark matter halo structure formation?","Question",{"text":75,"@type":76},"The matter density field properties in the initial conditions determine key characteristics of the Universe’s large-scale structure observed today and influence how collapsed regions form halos.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What two halo-related quantities does the study infer using machine learning?",{"text":80,"@type":76},"It infers (1) dark matter halo classification linked to halo formation prediction and (2) dark matter halo formation via reconstruction of the Halo Mass Function (HMF).",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods performed best, and how efficiently are they trained?",{"text":84,"@type":76},"Random Forest and Neural Networks performed best for classifying dark matter particles. The models can also reconstruct the HMF effectively using only a few data points, yielding a highly accurate fitting function aligned with simulations and theory.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]