[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124636-en":3,"doc-seo-124636-105":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},124636,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","C2-GaMe - Classification of Cluster Galaxy Membership with Machine Learning","C2-GaMe is a galaxy-cluster membership classification algorithm built on a suite of machine-learning models that assigns galaxies to orbiting, infalling, or background interloper populations using phase-space information. Trained and tested on UniverseMachine mock catalogs from Multi-Dark Planck 2 N-body simulations, it shows probabilistic classification yields more reliable cluster physical property estimates than deterministic approaches, including density profiles and velocity dispersion. The method uses proposed unbiased estimators and recovers orbiting and infalling position–velocity distributions with \u003C1% statistical error under interlopers in projected phase space. It further demonstrates robustness on a different simulation, and improves performance by adding specific star formation rate and the galaxy-to-cluster halo mass ratio, enabling applications to cluster cosmology and galaxy quenching.","ScienceDirect  \n[www.sciencedirect.com](www.sciencedirect.com)  \nAstronomy and Computing 00 (2023) 1–18  \nC2-GaMe: Classification of Cluster Galaxy Membership with Machine  \nLearning  \nAug 2023  \nDaniel Farida , Han Aungb , Daisuke Nagaib , Arya Farahic, Eduardo Rozod  \na Program in Applied Mathematics, Yale University, CT 06511, New Haven, USA b Department of Physics, Yale University, CT 06520, New Haven, USAc Department of Statistics and Data Science, The University of Texas, TX 78712, Austin, USA d Department of Physics, University of Arizona, AZ 85721, Tucson, USA  \nAbstract  \nWe present Classification of Cluster Galaxy Members (C2-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using phase space information as input. We train and test C2-GaMe with the galaxies from UniverseMachine mock catalog based on Multi-Dark Planck 2 N-body simulations. We show that probabilistic classification is superior to deterministic classification in estimating the physical properties of clusters, including density profiles and velocity dispersion. We propose a set of estimators to get an unbiased estimation of cluster properties. We demonstrate that C2-GaMe can recover the distribution of orbiting and infalling galaxies’ position and velocity distribution with \u003C 1% statistical error when using probabilistic predictions in the presence of interlopers in the projected phase space. Additionally, we demonstrate the robustness of trained models by applying them to a different simulation. Finally, adding a specific star formation rate and the ratio of the galaxy’s halo mass to the cluster’s halo mass as additional features improves the classification performance. We discuss potential applications of this technique to enhance cluster cosmology and galaxy quenching.  \nKeywords: methods: numerical, galaxies: clusters: general, dark matter, large-scale structure of Universe, cosmology: theory  \n1. Introduction  \nGalaxy clusters reside in the most massive gravitationally bound dark matter halos. They are unique laboratories for measuring the gravitational interactions in the universe that directly influence the collapse and growth of these large-scale structures [37] . Upcoming spectroscopic surveys, such as DESI, promise to provide unprecedented data, enabling spectroscopy of hundreds of thousands of galaxies [17] . Spectroscopic measurements of galaxies around galaxy clusters allow us to estimate the dynamics of these galaxies. Dynamics of cluster galaxies, such as the velocity dispersion inside the clusters [22, 14] or the infall velocity into the clusters [29], are potentially powerful mass prox-  \nies [5] . However, the spatial distinction to include all orbiting galaxies extends much further than the traditional radii definition as the backsplash galaxies exist beyond virial radii [8, 45, 26, 43, 63], and the galaxies inside the cluster radii can also be on their first infall. Observationally, these two populations are mixed along with interlopers, which are in front or behind the halo of interest but appear closer because of the projection and blend into the cluster we want to study [e.g., 61, 13] . Any contaminating interlopers can significantly bias the dynamical mass estimate by 10-15%[68] . Several methods for removing interlopers by treating individual clusters separately [67] or from a stacked sample of samples [55] exist in the literature. These methods cannot remove all interlopers while  \narXiv :2205 .0 1700v2 [ astro-ph .CO] 3  \nies. They are also unique probes of the large-scale inflow to probe modified gravity [38, 70] . However, using these dynamical measurements for cosmology requires a detailed understanding and control of the associated systematic uncertainties.  \nThe phase space structure of the halos consists of kinematically distinct populations of infalling and orbiting galax-  \nretaining all the member","cbCaieyjuzP1LuVM","https://ap.wps.com/l/cbCaieyjuzP1LuVM","pdf",1502696,1,18,"English","en",105,"# Abstract\n# 1. Introduction\n## Galaxy cluster dynamics and interlopers\n## Phase-space structure and dynamical mass\n## Edge radius and splashback connections\n## Machine learning in astrophysical classification\n## Related work on phase-space classifiers","[{\"question\":\"C2-GaMe如何区分轨道星系、并入星系和背景干扰星系？\",\"answer\":\"C2-GaMe使用相空间信息作为输入，结合一组机器学习模型对星系进行概率式分类，从而区分轨道、并入以及背景（干扰者）群体。\"},{\"question\":\"与确定性分类相比，概率式分类带来了什么优势？\",\"answer\":\"文中指出，概率式分类在估计集团的物理性质方面优于确定性分类，包括密度剖面和速度色散等关键量。\"},{\"question\":\"加入哪些额外特征会提升分类性能？\",\"answer\":\"在特征中加入特定的恒星形成率（specific star formation rate）以及星系暗晕质量与星团暗晕质量之比（halo mass ratio）能够进一步提升分类效果。\"}]","C2-GaMe - Classification of Cluster Galaxy Membership with Machine Learning | PDF",1785893448,45,{"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},"c2-game-classification-of-cluster-galaxy-membership-with-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/c2-game-classification-of-cluster-galaxy-membership-with-machine-learning/124636/",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},"C2-GaMe如何区分轨道星系、并入星系和背景干扰星系？","Question",{"text":75,"@type":76},"C2-GaMe使用相空间信息作为输入，结合一组机器学习模型对星系进行概率式分类，从而区分轨道、并入以及背景（干扰者）群体。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"与确定性分类相比，概率式分类带来了什么优势？",{"text":80,"@type":76},"文中指出，概率式分类在估计集团的物理性质方面优于确定性分类，包括密度剖面和速度色散等关键量。",{"name":82,"@type":73,"acceptedAnswer":83},"加入哪些额外特征会提升分类性能？",{"text":84,"@type":76},"在特征中加入特定的恒星形成率（specific star formation rate）以及星系暗晕质量与星团暗晕质量之比（halo mass ratio）能够进一步提升分类效果。","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"]