Scholarlink

<p>This study explores the stability of an <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10509_2025_4480_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="58" /> </InlineMediaObject> <EquationSource Format="MATHML"><math> <mi>f</mi> <mo stretchy="false">(</mo> <mi>R</mi> <mo>,</mo> <mi>T</mi> <mo stretchy="false">)</mo> </math></EquationSource> <EquationSource Format="TEX">$f(R,T)$</EquationSource> </InlineEquation> 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 supernova 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Numerical and statistical insights into \(f(R,T)\) cosmology: GRP, RK4, and MLE approaches

  • 期刊论文

  • 发表日期：

    2025年8月29日

查看全文
  • Navya Jain,
  • R. K. Mishra

摘要

This study explores the stability of an f ( R , T ) $f(R,T)$ 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 supernova 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.