[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122394-en":3,"doc-seo-122394-105":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},122394,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Gamma-ray burst redshift estimation using machine learning and the associated web app","Gamma-ray bursts (GRBs) observed at very high redshifts up to 9.4 offer powerful probes of the distant Universe, yet only a small fraction have measured redshifts because of observational biases. The study targets this limitation by extending an ensemble supervised machine learning training set with 30 additional GRBs to improve pseudo-redshift accuracy. A freely accessible web application estimates the redshift of long GRBs with plateau emission by inputting GRB parameters, enabling community-wide rapid pseudo-redshift inference. Using X-ray afterglow parameters from the Neil Gehrels Swift Observatory, the model estimates redshifts for 276 LGRBs and increases the long-GRB known-redshift sample by 110%, supported by Monte Carlo simulations.","Aht©&tpT:,/698doAuith,oA9rg/ors21020(2021051255/0) 004-6361/202452651 &~~t~~ronomy~~t~~rop~~h~~ys~~i~~cs  \nGamma-ray burst redshift estimation using machine learning and the associated web app  \nA. Narendra 1 , 2 , ⋆, M. G. Dainotti3 , 4 , 5 , 6 , 7 , ⋆ , ⋆⋆, M. Sarkar9, A. Ł . Lenart 1, M. Bogdan 12 , 13, A. Pollo 1 , 8, B. Zhang 11, A. Rabeda 1, V. Petrosian 10, and K. Iwasaki3 , 4 , 14  \n1 Astronomical Observatory of Jagiellonian University in Kraków, Orla 171, 30-244 Kraków, Poland  \n2 Jagiellonian University, Doctoral School of Exact and Natural Sciences, Krakow, Poland  \n3 Division of Science, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  \n4 The Graduate University for Advanced Studies (SOKENDAI), Shonankokusaimura, Hayama, Miura District, Kanagawa 240-0115, Japan  \n5 Space Science Institute, 4765 Walnut St Ste B, Boulder, CO 80301, USA  \n6 Nevada Center for Astrophysics, University of Nevada, 4505 Maryland Parkway, Las Vegas, NV 89154, USA  \n7 Bay Environmental Institute, PO Box 25, Moffett Field, CA, USA  \n8 National Center for Nuclear Physics (NCBJ), Warsaw, Poland  \n9 Department of Physical Sciences, Indian Institute of Science Education and Research (IISER), Mohali, Punjab, India  \n10 Department of Physics and Kavli Institute of Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305, USA  \n11 University of Nevada, Las Vegas, 4505 S. Maryland Pkwy, Las Vegas, NV 89154, USA  \n12 Department of Mathematics, University of Wroclaw, 50-384 Wrocław, Poland  \n13 Department of Statistics, Lund University, 221 00 Lund, Sweden  \n14 Center for Computational Astrophysics, National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  \nReceived 17 October 2024 / Accepted 5 April 2025  \nABSTRACT  \nContext. Gamma-ray bursts (GRBs), which have been observed at redshifts as high as 9.4, could serve as valuable probes for investigating the distant Universe. However, using them in this manner necessitates an increase in the number of GRBs with determined redshifts, as currently only 12% of them have known redshifts due to observational biases.  \nAims. We aim to address the shortage of GRBs with measured redshifts to enable full realization of their potential as valuable cosmological probes.  \nMethods. Following our previous approach, in this work we take a further step to overcome this issue by adding 30 more GRBs to our ensemble supervised machine learning training sample, representing an increase of 20%, which will help us obtain more accurate pseudo-redshifts. In addition, we have built a freely accessible and user-friendly web application that infers the redshift of long GRBs (LGRBs) with plateau emission using our machine learning model. The web app is the first of its kind for such a study and will allow the community to obtain pseudo-redshifts by entering the GRB parameters into the app.  \nResults. Through our machine learning model, we successfully estimated redshifts for 276 LGRBs using X-ray afterglow parameters detected by the Neil Gehrels Swift Observatory and increased the sample of LGRBs with known redshifts by 110% . We also performed Monte Carlo simulations to demonstrate the future applicability of this research.  \nConclusions. The results presented in this work will enable the community to increase the sample of GRBs with known pseudoredshifts. This can help address many outstanding issues, such as GRB formation rate, luminosity function, and the true nature of low-luminosity GRBs, and it can enable the application of GRBs as standard candles.  \nKey words. methods: data analysis – techniques: photometric – distance scale – gamma rays: general  \n1. Introduction  \nGamma-ray bursts (GRBs) are the brightest and most powerful explosion events after the Big Bang and are observed across a wide range of redshifts, from 0.0085 (Galama et al. 1998) to 8.2 and 9.4 (Tanvir et al. 2009 ; Cucchiara et al. 2011) . This vast range of redshifts (","cbCaits5Dm4TMadZ","https://ap.wps.com/l/cbCaits5Dm4TMadZ","pdf",2608112,1,24,"English","en",105,"# Introduction\n# Methods\n## Machine learning training sample expansion\n## Web application for pseudo-redshift inference\n# Results\n## Redshift estimates for LGRBs\n## Monte Carlo simulations\n# Conclusions\n# Key words","[{\"question\":\"Why are pseudo-redshifts important for gamma-ray bursts?\",\"answer\":\"Because only a limited fraction of GRBs currently have known redshifts due to observational biases. Pseudo-redshifts enable larger samples for cosmological studies.\"},{\"question\":\"How does the machine learning model improve redshift estimation in this work?\",\"answer\":\"It expands the supervised training ensemble by adding 30 more GRBs, increasing the training sample by 20%, which yields more accurate pseudo-redshifts.\"},{\"question\":\"What inputs does the associated web app require, and what does it output?\",\"answer\":\"The web application accepts GRB parameters and infers the redshift for long GRBs with plateau emission, outputting pseudo-redshifts based on the machine learning model.\"}]","Gamma-ray burst redshift estimation using machine learning and the associated web app | PDF",1785810401,60,{"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},"gamma-ray-burst-redshift-estimation-using-machine-learning-and-the-associated-web-app","",{"@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/gamma-ray-burst-redshift-estimation-using-machine-learning-and-the-associated-web-app/122394/",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 are pseudo-redshifts important for gamma-ray bursts?","Question",{"text":75,"@type":76},"Because only a limited fraction of GRBs currently have known redshifts due to observational biases. Pseudo-redshifts enable larger samples for cosmological studies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning model improve redshift estimation in this work?",{"text":80,"@type":76},"It expands the supervised training ensemble by adding 30 more GRBs, increasing the training sample by 20%, which yields more accurate pseudo-redshifts.",{"name":82,"@type":73,"acceptedAnswer":83},"What inputs does the associated web app require, and what does it output?",{"text":84,"@type":76},"The web application accepts GRB parameters and infers the redshift for long GRBs with plateau emission, outputting pseudo-redshifts based on the machine learning model.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]