Numerical and Statistical Insights into f (R, T ) Cosmology: GRP, RK4, and MLE Approaches

Numerical and Statistical Insights into f (R, T ) Cosmology: GRP, RK4, and MLE Approaches | 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 Numerical and Statistical Insights into f (R, T ) Cosmology: GRP, RK4, and MLE Approaches Navya Jain, R. K. Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6841435/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Astrophysics and Space Science → Version 1 posted 10 You are reading this latest preprint version Abstract This study explores the stability of an f (R, T) gravity-based cosmological model using perturbation techniques and numerical methods. Stability conditions are examined by analyzing the growth rate of small perturbations in density and pressure. Furthermore, the Runge-Kutta fourth-order (RK4) method is employed to numerically track the evolution of these perturbations and validate the theoretical predictions. To ensure consistency with observational data, the model is tested against Hubble parameter measurements and the Pantheon Type Ia super-nova dataset using the Maximum Likelihood Estimation (MLE) method. The findings provide deeper insights into the stability of modified gravity models and underscore the significance of combining analytical, numerical, and observational approaches in cosmological studies. Stability Perturbation theory Growth rate parameter f(R T ) gravity Runge-Kutta method Full Text Additional Declarations No competing interests reported. Supplementary Files manuscript5.zip Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Astrophysics and Space Science → Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 30 Jun, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 07 Jun, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6841435","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469368400,"identity":"d774d983-fd54-4728-92b6-230e2b873cb5","order_by":0,"name":"Navya Jain","email":"","orcid":"","institution":"Sant Longowal Institute of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Navya","middleName":"","lastName":"Jain","suffix":""},{"id":469368401,"identity":"607c37ea-3bc4-4ea0-aaaf-529159393f9e","order_by":1,"name":"R. K. 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