[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124628-en":3,"doc-seo-124628-105":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},124628,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning Classification of Repeating FRBs from FRB121102 - Research Findings","Fast Radio Bursts (FRBs) are millisecond radio flashes whose physical origin remains unclear, especially for repeating FRBs where meaningful classification can constrain underlying mechanisms. This study analyzes 1652 repeating FRBs from FRB121102 detected by FAST and applies an unsupervised machine learning model with tuned hyperparameters. The results indicate four clusters for FRB121102 instead of the two clusters proposed in earlier literature, with the “Atypical” group split into three sub-clusters. The clustering uses more physical parameters and provides a more comprehensive, physically grounded framework.","arXiv :2307 .02811v1 [ astro-ph .HE] 6 Jul 2023  \nMachine Learning Classification of Repeating FRBs from FRB121102  \n★  \nBjorn Jasper R. Raquel 1,2,3 , Tetsuya Hashimoto2 , Tomotsugu Goto4 , Bo Han Chen4,5,6 , Yuri Uno2 , Tiger Yu-Yang Hsiao4,7 , Seong Jin Kim4 , and Simon C.-C. Ho4,8  \n1 Department of Earth and Space Sciences, Rizal Technological University, Boni Avenue, Mandaluyong City, 1550 Metro Manila, Philippines  \n2 Department of Physics, National Chung Hsing University, No. 145, Xingda Rd., South Dist., Taichung, 40227, Taiwan (R.O.C.)  \n3 National Institute of Physics, College of Science, University of the Philippines, Diliman, Quezon City, 1101 Metro Manila, Philippines  \n4 Institute of Astronomy, National Tsing Hua University, 101, Section 2 . Kuang-Fu Road, Hsinchu, 30013, Taiwan (R.O.C.)  \n5 Department of Physics, National Tsing Hua University, 101, Section 2 . Kuang-Fu Road, Hsinchu, 30013, Taiwan (R.O.C.)  \n6 Graduate School of Data Science, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea  \n7 Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA  \n8 Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia  \nAccepted 2023 June 15 . Received 2023 June 12; in original form 2022 October 9  \nABSTRACT  \nFast Radio Bursts (FRBs) are mysterious bursts in the millisecond timescale at radio wavelengths. Currently, there is little understanding about the classification of repeating FRBs, based on difference in physics, which is of great importance in understanding their origin. Recent works from the literature focus on using specific parameters to classify FRBs to draw inferences on the possible physical mechanisms or properties of these FRB subtypes. In this study, we use publicly available 1652 repeating FRBs from FRB121102 detected with the Five-hundred-meter Aperture Spherical Telescope (FAST), and studied them with an unsupervised machine learning model. By fine-tuning the hyperparameters of the model, we found that there isan indication for four clusters from the bursts of FRB121102 instead of the two clusters (\"Classical\" and \"Atypical\") suggested in the literature. Wherein, the “Atypical” cluster can be further classified into three sub-clusters with distinct characteristics. Our findings show that the clustering result we obtained is more comprehensive not only because our study produced results which are consistent with those in the literature but also because our work uses more physical parameters to create these clusters. Overall, our methods and analyses produced a more holistic approach in clustering the repeating FRBs of FRB121102 .  \nKey words: (transients:) fast radio bursts – stars: magnetars – stars: neutron – methods: data analysis  \n1 INTRODUCTION  \nFast Radio Bursts (FRBs) are bright millisecond-duration radio flashes of extragalactic origin (Lorimer et al. 2007; Thornton et al. 2013; Petroff et al. 2016) . They are characterized by their anomalously high dispersion measure (DM) and millisecond duration, indicating high brightness temperature and isotropic energy release (Ravi et al. 2015; Tendulkar et al. 2017; Zhang 2018; Bannister et al. 2019; Ravi et al. 2019; Li et al. 2021b; Bochenek et al. 2020) . FRBs are usually classified as either ‘repeating’ or ‘non-repeating.’ Repeating FRBs have multiple bursts, while non-repeating FRBs have one-off bursts (Cordes & Chatterjee 2019) . Currently, there are > 600 FRBs that are reported as of April 2022 (Petroff et al. 2016; Li et al. 2021b; CHIME/FRB Collaboration et al. 2021) .  \nFRB121102, first discovered in 2014 (Spitler et al. 2014) and identified as a repeater in 2016 (Spitler et al. 2016), is the most extensively studied FRB across a broad range of radio frequencies from 600 MHz up to 8 GHz (Josephy et al. 2019; Gajjar et al. 2018).The repetition allowed for localization with a high precision of 100 mas, leading to the first unambiguous identification o","cbCaitAUl2swa9ft","https://ap.wps.com/l/cbCaitAUl2swa9ft","pdf",8863935,1,24,"English","en",105,"# Abstract\n# Introduction\n## Repeating vs non-repeating FRBs\n## FRB121102 background and observations\n## Machine learning in FRB research","[{\"question\":\"What problem does this study address about repeating FRBs?\",\"answer\":\"It targets the limited understanding of how repeating FRBs should be classified and what physical differences distinguish their subtypes.\"},{\"question\":\"How is the dataset and method set up in the study?\",\"answer\":\"The work uses 1652 repeating FRBs from FRB121102 detected with FAST and applies an unsupervised machine learning model, with tuned hyperparameters.\"},{\"question\":\"What clustering outcome does the study find for FRB121102?\",\"answer\":\"Instead of two clusters (“Classical” and “Atypical”), the model suggests four clusters, and the “Atypical” cluster further divides into three sub-clusters with distinct characteristics.\"}]","Machine Learning Classification of Repeating FRBs from FRB121102 - 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