[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122503-en":3,"doc-seo-122503-105":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":20,"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},122503,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Classifying binary black holes from Population III stars with the Einstein Telescope - A machine-learning approach","Third-generation gravitational-wave detectors such as the Einstein Telescope (ET) will observe binary black hole mergers at very high redshifts, but determining the astrophysical origin of such events remains uncertain due to low signal-to-noise ratios and limited luminosity-distance accuracy. The study introduces a machine-learning framework to infer whether high-redshift BBHs originate from Population III stars rather than Population I-II stars. It models population uncertainties, estimates parameter errors via Fisher information, and performs classification using XGBoost, reporting high-precision probabilistic identification for mock detections while enabling key ET science goals.","A&A, 690, A362 (2024)  \n[https:](https://doi.org/10.1051/0004-6361/202450381)[//](https://doi.org/10.1051/0004-6361/202450381)[doi.org](https://doi.org/10.1051/0004-6361/202450381)[/](https://doi.org/10.1051/0004-6361/202450381)[10.1051](https://doi.org/10.1051/0004-6361/202450381)[/](https://doi.org/10.1051/0004-6361/202450381)[0004-6361](https://doi.org/10.1051/0004-6361/202450381)[/](https://doi.org/10.1051/0004-6361/202450381)[202450381](https://doi.org/10.1051/0004-6361/202450381)  \n The Authors 2024  \n&~~t~~ronomy~~t~~rop~~h~~ys~~i~~cs  \nClassifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach  \nFilippo Santoliquido 1,2 ; ?, Ulyana Dupletsa 1,2, Jacopo Tissino 1,2, Marica Branchesi 1,2, Francesco Iacovelli3 ,4, Giuliano Iorio5 ,6, Michela Mapelli7 ,5 ,6, Davide Gerosa8 ,9 , 10, Jan Harms 1,2, and Mario Pasquato5 ,6 , 11 , 12 , 13  \n1 Gran Sasso Science Institute (GSSI), 67100 L'Aquila, Italy  \n2 INFN, Laboratori Nazionali del Gran Sasso, 67100 Assergi, Italy  \n3 Département de Physique Théorique, Université de Genève, 24 quai Ernest Ansermet, 1211 Genève, Switzerland  \n4 Gravitational Wave Science Center (GWSC), Université de Genève, 1211 Genève, Switzerland  \n5 Dipartimento di Fisica e Astronomia “G. Galilei”, Università degli studi di Padova, Vicolo dell'Osservatorio 3, 35122 Padova, Italy  \n6 INFN, Sezione di Padova, Via Marzolo 8, 35131 Padova, Italy  \n7 Institut für Theoretische Astrophysik, ZAH, Universität Heidelberg, Albert-Ueberle-Str. 2, 69120 Heidelberg, Germany  \n8 Dipartimento di Fisica “G. Occhialini”, Università degli studi di Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy  \n9 INFN, Sezione di Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy  \n10 School of Physics and Astronomy & Institute for Gravitational Wave Astronomy, University of Birmingham, Birmingham B15 2TT, United Kingdom  \n11 Département de Physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Canada  \n12 Mila – Quebec Artificial Intelligence Institute, 6666 Rue Saint-Urbain, Montréal, Canada  \n13 Ciela – Montréal Institute for Astrophysical Data Analysis and Machine Learning, Montréal, Canada  \nReceived 15 April 2024 / Accepted 21 August 2024  \nABSTRACT  \nThird-generation (3G) gravitational-wave detectors such as the Einstein Telescope (ET) will observe binary black hole (BBH) mergersat redshifts up to z 􀀘 100. However, an unequivocal determination of the origin of high-redshift sources will remain uncertain because of the low signal-to-noise ratio (S/N) and poor estimate of their luminosity distance. This study proposes a machine-learning approach to infer the origins of high-redshift BBHs. We specifically di􀀋erentiate those arising from Population III (Pop. III) stars, which probably are the first progenitors of star-born BBH mergers in the Universe, and those originated from Population I-II (Pop. I–II) stars. We considered a wide range of models that encompass the current uncertainties on Pop. III BBH mergers. We then estimated the parameter errors of the detected sources with ET using the Fisher information-matrix formalism, followed by a classification using XGBoost, which is a machine-learning algorithm based on decision trees. For a set of mock observed BBHs, we provide the probability that they belong to the Pop. III class while considering the parameter errors of each source. In our fiducial model, we accurately identify &10% of the detected BBHs that originate from Pop. III stars with a precision >90% . Our study demonstrates that machine-learning enables us to achieve some pivotal aspects of the ET science case by exploring the origin of individual high-redshift GW observations. We set the basis for further studies, which will integrate additional simulated populations and account for further uncertainties in the population modeling.  \nKey words. black hole physics – gravitational waves – methods: numerical – methods: statistical – sta","cbCaiaCBwuXOgy1J","https://ap.wps.com/l/cbCaiaCBwuXOgy1J","pdf",8011246,1,14,"English","en",105,"# Abstract\n# Introduction\n## Einstein Telescope observational prospects\n## Challenges in high-redshift origin inference\n## Motivation for machine learning","[{\"question\":\"Why is inferring the origin of high-redshift BBH mergers difficult for the Einstein Telescope?\",\"answer\":\"Because detections at high redshift often have low signal-to-noise ratio and lead to poor luminosity-distance estimation, making origin inference uncertain.\"},{\"question\":\"What is the proposed method for identifying Population III origins?\",\"answer\":\"The approach uses Fisher information-matrix parameter-error estimation for ET detections, then applies XGBoost to classify whether sources belong to the Population III class, producing event-by-event probabilities.\"},{\"question\":\"How accurate is the Population III classification in the study’s fiducial model?\",\"answer\":\"In the fiducial model, it identifies about 10% of detected BBHs originating from Population III stars with precision greater than 90%.\"}]","Classifying binary black holes from Population III stars with the Einstein Telescope - A machine-learning approach | PDF",1785810980,35,{"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},"classifying-binary-black-holes-from-population-iii-stars-with-the-einstein-telescope-a-machine-learning-approach","",{"@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/classifying-binary-black-holes-from-population-iii-stars-with-the-einstein-telescope-a-machine-learning-approach/122503/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is inferring the origin of high-redshift BBH mergers difficult for the Einstein Telescope?","Question",{"text":75,"@type":76},"Because detections at high redshift often have low signal-to-noise ratio and lead to poor luminosity-distance estimation, making origin inference uncertain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the proposed method for identifying Population III origins?",{"text":80,"@type":76},"The approach uses Fisher information-matrix parameter-error estimation for ET detections, then applies XGBoost to classify whether sources belong to the Population III class, producing event-by-event probabilities.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the Population III classification in the study’s fiducial model?",{"text":84,"@type":76},"In the fiducial model, it identifies about 10% of detected BBHs originating from Population III stars with precision greater than 90%.","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"]