[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117793-en":3,"doc-seo-117793-105":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},117793,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Cosmology with Galaxy Cluster Properties using Machine Learning","Galaxy clusters act as the universe’s largest gravitational structures, linking their mass assembly to the underlying cosmology through observable properties. Their mass function, baryon fraction, and mass distribution are used to infer cosmological parameters, yet complex scaling relations and systematics reduce the full potential of clusters as a cosmological probe. This work proposes an ML method trained on simulated cluster observables to predict key cosmological parameters reliably, enabling unbiased constraints.","Astronomy & Astrophysics manuscript no. main ©ESO 2023  \nApril 19, 2023  \nCosmology with Galaxy Cluster Properties using Machine Learning  \narXiv :2304 .09142v1 [ astro-ph .CO] 18 Apr 2023  \nLanlan Qiu 1; 2 , Nicola R. Napolitano 1; 2 ? , Stefano Borgani3; 4; 5; 6; 7 , Fucheng Zhong 1; 2?? , Xiaodong Li1; 2 , Mario Radovich8 , Weipeng Lin 1; 2 , Klaus Dolag9; 10 , Crescenzo Tortora 11 , Yang Wang2; 12 , Rhea-Silvia Remus9 ,  \nGiuseppe Longo 13  \n1 School of Physics and Astronomy, Sun Yat-sen University Zhuhai Campus, 2 Daxue Road, Tangjia, Zhuhai 519082, P.R. China  \n2 CSST Science Center for Guangdong-Hong Kong-Macau Great Bay Area, Zhuhai 519082, P.R. China  \n3 Astronomy Unit, Department of Physics, University of Trieste, via Tiepolo 11, I-34131 Trieste, Italy  \n4 INAF-Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, I-34143 Trieste, Italy  \n5 IFPU, Institute for Fundamental Physics of the Universe, Via Beirut 2, 34014 Trieste, Italy  \n6 INFN, Instituto Nazionale di Fisica Nucleare, Via Valerio 2, I-34127, Trieste, Italy  \n7 ICSC-Italian Research Center on High Performance Computing, Big Data and Quantum Computing  \n8 INAF-Osservatorio Astronomico di Padova, via dell'Osservatorio 5, 35122 Padova, Italy  \n9 Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians-Universität München, Scheinerstr. 1, 81679 München, Germany  \n10 Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Straße 1, 85741 Garching, Germany  \n11 INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, 80131-Napoli, Italy  \n12 Peng Cheng Laboratory, No.2, Xingke 1st Street, Shenzhen, 518000, P. R. China  \n13 Department of Physics E. Pancini, University Federico II, Via Cinthia 6, 80126-I, Naples, Italy  \nApril 19, 2023  \nABSTRACT  \nContext. Galaxy clusters are the largest gravitating structures in the universe and their mass assembly is sensitive to the underlying cosmology. Their mass function, baryon fraction, and mass distribution have been used to infer cosmological parameters, despite the presence of systematics. However, the complexity of the scaling relations among galaxy cluster properties has never been fully exploited, limiting their potential as a cosmological probe.  \nAims. We propose the first Machine Learning (ML) method using galaxy cluster properties from hydrodynamical simulations in di􀀋erent cosmologies to predict cosmological parameters combining a series of canonical cluster observables, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, and velocity dispersion at di􀀋erent redshifts. Methods. The machine learning model is trained on mock “measurements” of these observable quantities from Magneticum multicosmology simulations to derive unbiased constraints on a set of cosmological parameters. These include the mass density parameter,􀀊m , the power spectrum normalization, 􀀛8 , the baryonic density parameter, 􀀊b , and the reduced Hubble constant, h0.  \nResults. We test the ML model on catalogs of a few hundred clusters taken, in turn, from each simulation and find that the ML model can correctly predict the cosmology they have been picked from. The cumulative accuracy depends on the cosmology, ranging from 21% to 75% . We demonstrate that this is su􀀎cient to derive unbiased constraints on the main cosmological parameters with errors of the order of 􀀘 14% for 􀀊m , 􀀘 8% for 􀀛8 , 􀀘 6% for 􀀊b , and 􀀘 3% for h0.  \nConclusions. This proof-of-concept analysis, yet based on a limited variety of multi-cosmology simulations, shows that machine learning can e􀀎ciently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a given cosmological parameter combination. More largevolume, mid-resolution, multi-cosmology hydro-simulations need to be produced to expand the applicability to a wider cosmological parameter range. However, this first test is exc","cbCailydKYg5tWlj","https://ap.wps.com/l/cbCailydKYg5tWlj","pdf",7264936,1,18,"English","en",105,"# Abstract\n## Introduction\n## Aims and Methods\n## Results and Conclusions","[{\"question\":\"How does the proposed machine learning method use galaxy cluster observables?\",\"answer\":\"It trains on mock measurements of canonical cluster observables drawn from hydrodynamical simulations, combining gas and stellar properties, cluster size, total mass, and velocity dispersion at different redshifts to predict cosmological parameters.\"},{\"question\":\"Which cosmological parameters are predicted in the study?\",\"answer\":\"The method targets the mass density parameter (Ωm), the power spectrum normalization (σ8), the baryonic density parameter (Ωb), and the reduced Hubble constant (h0).\"},{\"question\":\"What level of accuracy does the model achieve when tested on simulated cluster catalogs?\",\"answer\":\"When applied to catalogs from different simulations, the model can correctly identify the cosmology with cumulative accuracy ranging from about 21% to 75%, yielding unbiased parameter constraints with uncertainties on the order of ~14% (Ωm), ~8% (σ8), ~6% (Ωb), and ~3% (h0).\"}]","Cosmology with Galaxy Cluster Properties using Machine Learning | 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does the proposed machine learning method use galaxy cluster observables?","Question",{"text":75,"@type":76},"It trains on mock measurements of canonical cluster observables drawn from hydrodynamical simulations, combining gas and stellar properties, cluster size, total mass, and velocity dispersion at different redshifts to predict cosmological parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which cosmological parameters are predicted in the study?",{"text":80,"@type":76},"The method targets the mass density parameter (Ωm), the power spectrum normalization (σ8), the baryonic density parameter (Ωb), and the reduced Hubble constant (h0).",{"name":82,"@type":73,"acceptedAnswer":83},"What level of accuracy does the model achieve when tested on simulated cluster catalogs?",{"text":84,"@type":76},"When applied to catalogs from different simulations, the model can correctly identify the cosmology with cumulative accuracy ranging from about 21% to 75%, yielding unbiased parameter constraints with uncertainties on the order of ~14% (Ωm), ~8% (σ8), ~6% (Ωb), and ~3% (h0).","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 & 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