[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123724-en":3,"doc-seo-123724-105":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},123724,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Identifying the physical origin of gamma-ray bursts with supervised machine learning","Empirical duration-based classification of gamma-ray bursts (GRBs) into long and short categories is widely used, and is often linked to different physical origins: compact binary mergers (Type I) versus massive-star collapses (Type II). Significant overlap and “intermingled” GRBs—short-duration Type II and long-duration Type I—show that duration alone is insufficient. A supervised machine-learning approach, chiefly XGBoost, is developed using GRB prompt emission, afterglow, and host-galaxy features. Prompt emission yields the strongest separation, with key discriminants including T90, hardness ratio, and fluence, enabling probabilistic assignment of physical classes to currently unclassified GRBs.","arXiv :2211 . 16451v2 [ astro-ph .HE] 15 Oct 2023  \nDraft version October 17, 2023  \nTypeset using LATEX default style in AASTeX631  \nIdentifying the physical origin of gamma-ray bursts with supervised machine learning  \nJia-Wei Luo ,1, 2, 3 Fei-Fei Wang,4 Jia-Ming Zhu-Ge,2, 3 Ye Li,5 Yuan-Chuan Zou ,6 and Bing Zhang2, 3  \n1 College of Physics and Hebei Key Laboratory of Photophysics Research and Application, Hebei Normal University, Shijiazhuang, Hebei 050024, China  \n2 Nevada Center for Astrophysics, University of Nevada, Las Vegas, NV 89154, USA  \n3 Department of Physics and Astronomy, University of Nevada Las Vegas, NV 89154, USA  \n4 School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China  \n5 Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 100012, China  \n6 Department of Astronomy, School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China  \nABSTRACT  \nThe empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multi-parameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner’s GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing feature in prompt emission to be T90 , hardness ratio, and fluence. After building the machine learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.  \nKeywords: Gamma-ray bursts(629)– Astronomy data analysis(1858)  \n1. INTRODUCTION  \nDating from the early days of gamma-ray burst (GRB) study, a clear bimodal distribution had been identified in their durations (Kouveliotou et al. 1993) . Two classes of GRBs are then proposed based on their durations, namely long GRBs (LGRBs) and short GRBs (SGRBs) . The commonly used criterion is based on T90 , the time within which 90% of the fluence of the GRB is observed, with the dividing point set to be T90 = 2s.  \nLGRBs are thought to be produced by the core-collapse of massive stars (Woosley 1993), and this theory is subsequently supported by direct observational evidence of the association of some LGRBs with Type Ic supernovae (Galama et al. 1998; Woosley & Bloom 2006) . SGRBs are thought to be originated from compact star mergers (Eichler et al. 1989), and this theory is supported by the multi-messenger observations of the binary neutron star merger event GW170817/GRB 170817A (Abbott et al. 2017a,b,c; Goldstein et al. 2017; Zhang et al. 2018) .  \nHowever, this dichotomy is far from perfect. Significant overlap presents in the duration distributions of long and short GRBs, and the duration itself is dependent on the energy band in which it is measured (Mukherjee et al. 1998; Hakkila et al. 2003; Horv´ath et al. 2006; Zhang & Choi 2008; Veres et al. 2010; Qin et al. 2012; Bromberg et al. 2013;  \nCorresponding author: Jia-Wei Luo  \n[ljw@hebtu.edu.cn](ljw@hebtu.edu.cn)  \n2 J-W Luo et al.  \nZhang et al. 2016) . Moreover, there are some short-duration GR","cbCaij4scot6KMyC","https://ap.wps.com/l/cbCaij4scot6KMyC","pdf",1540796,1,23,"English","en",105,"# Introduction\n## Motivation: limitations of duration-only GRB classes\n## Physical classification scheme (Type I vs Type II)\n# Method Overview\n## Feature grouping: prompt emission, afterglow, host galaxy\n## Supervised learning with XGBoost","[{\"question\":\"Why is duration-based GRB classification insufficient on its own?\",\"answer\":\"Long and short GRBs show substantial overlap in their duration distributions, and observations include intermingled cases such as short-duration Type II and long-duration Type I bursts. These findings indicate duration alone cannot reliably map to physical origin.\"},{\"question\":\"How does the paper build the supervised machine-learning classification scheme?\",\"answer\":\"It applies supervised machine learning, primarily XGBoost, using data drawn from the GRB Big Table and Greiner’s GRB catalog. Input features are grouped into three subgroups: prompt emission, afterglow, and host galaxy.\"},{\"question\":\"Which features are most important for distinguishing Type I and Type II GRBs?\",\"answer\":\"The prompt emission subgroup performs best for separating the two types. Within prompt emission, the most important distinguishing features are T90, hardness ratio, and fluence.\"}]","Identifying the physical origin of gamma-ray bursts with supervised machine learning | PDF",1785818201,58,{"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},"identifying-the-physical-origin-of-gamma-ray-bursts-with-supervised-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/identifying-the-physical-origin-of-gamma-ray-bursts-with-supervised-machine-learning/123724/",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},"Why is duration-based GRB classification insufficient on its own?","Question",{"text":75,"@type":76},"Long and short GRBs show substantial overlap in their duration distributions, and observations include intermingled cases such as short-duration Type II and long-duration Type I bursts. These findings indicate duration alone cannot reliably map to physical origin.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper build the supervised machine-learning classification scheme?",{"text":80,"@type":76},"It applies supervised machine learning, primarily XGBoost, using data drawn from the GRB Big Table and Greiner’s GRB catalog. Input features are grouped into three subgroups: prompt emission, afterglow, and host galaxy.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features are most important for distinguishing Type I and Type II GRBs?",{"text":84,"@type":76},"The prompt emission subgroup performs best for separating the two types. Within prompt emission, the most important distinguishing features are T90, hardness ratio, and fluence.","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"]