Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning

Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning Nan Liang, Bin zhang, Huifeng Wang, Xiaodong Nong, Guangzhen Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4944717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2025 Read the published version in Astrophysics and Space Science → Version 1 posted 8 You are reading this latest preprint version Abstract In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation (Ep-Eiso) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process. gamma-ray bursts: general cosmology: observations Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2025 Read the published version in Astrophysics and Space Science → Version 1 posted Editorial decision: Revision requested 09 Nov, 2024 Reviews received at journal 09 Nov, 2024 Reviewers agreed at journal 09 Nov, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers invited by journal 26 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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