arXiv subjects

Suraj Kumar Behera

Publications and source records attributed to Suraj Kumar Behera.

5 recordsLinked to original sources

arXiv2026

$f(T,\mathcal{L}_m)$ Cosmology Embedded in a Viscous Barotropic Fluid

We examine the cosmological dynamics of a viscous fluid within the framework of $f(T,\mathcal{L}_m)$ gravity by using a barotropic equation of state for cosmic fluid. The modified Friedmann equations are used to construct an analytical Hubble model. Then $H(z)$, Pantheon+SH0ES, DESI DR II BAO, and Cosmic Microwave Background datasets are used in a Bayesian Markov Chain Monte Carlo analysis to constrain its free parameters. Physically viable estimations of the Hubble constant and barotropic equation of state parameter are given by the obtained best-fit values. Based on the constrained parameters, we examine the redshift dependence of deceleration parameter, effective equation of state, dark energy equation of state, the effective pressure and viscous pressure quantities. While the deceleration parameter reveals how the universe evolved from an initially decelerating state to its present accelerating expansion, both equation of state parameters persist in the quintessence regime in the late epoch. Moreover, throughout the cosmic evolution, the viscous and effective pressures stay negative. The observational viability of the suggested viscous $f(T,\mathcal{L}_m)$ cosmological model is supported by the estimated Hubble constant, whose values agree well with the results from independent cosmological observations.

physics.gen-ph↗

arXiv2026

Cosmological Parameters in $f(T)$ Gravity: Theoretical and Observational Analysis

The $f(T)$ gravity is one of the extensions of teleparallel equivalent of general relativity, in which more general functions of the torsion scalar $T$ can be described. With the proposed functional form of $f(T) = \alpha T - \beta u^{-n} + \gamma u^m$, where $u = (-T/6)$, we have analyzed the cosmological parameters using dynamical system analysis and cosmological datasets. The dynamical behavior of this model is analyzed with phase-space analysis by transforming the cosmological equations into an autonomous system. Critical points are identified, and their stability conditions examined, enabling the classifications of the early and late-time evolutionary phases of the Universe. The stability conditions are further demonstrated by phase-portrait diagrams that highlight transitions between radiation, matter, and dark-energy-dominated epochs. Then we used the Markov Chain Monte Carlo statistical technique to constrain the model parameters with the recent observational dataset, such as DESI DR2 BAO, and its combination with the Hubble and Pantheon+SH0ES data. The best-fit values for the model parameters were obtained by data analysis, $m \equiv 0.91^{+0.07}_{-0.09}$ and $n \equiv 0.69^{+0.09}_{-0.08}$, and are well within the stability range obtained ($m<1\land n>-1$) through dynamical system analysis. The combined theoretical and observational analysis shows that the proposed $f(T)$ gravity model successfully reproduces the observed cosmic expansion history of the Universe.

gr-qc↗

arXiv2026

Reconstruction of Accelerating Nonlinear $f(T)$ Gravity Models via Hybrid Scale Factor: Cosmological Dynamics and Bayesian Evidence

This study offers a comprehensive reconstruction of $f(T)$ gravity model with three distinct non-linear as well as novel forms employing a hybrid scale factor to depict the expansion history of the universe starting from early decelerated epoch to late-time accelerated evolution. Model parameters are rigorously constrained using the Monte Carlo Markov Chain (MCMC) analysis with the help of Bayesian statistics and incorporating late-time observations from BAO and Patheon+SH0ES. The investigation of dynamical parameters such as the equation of state parameter and cosmological parameters indicates alignment with an accelerated expansion phase in both the present and late time epochs. Validation is conducted by assessing the energy conditions, verifying the feasibility of the model forms with particular emphasis on the violation of the strong energy condition that indicates dark energy dominance in modified gravity scenarios. This investigation has been instrumental in determining models that remain consistent with cosmological observations and theoretical requirements. The reconstructed forms of the model effectively mimic $\Lambda$CDM at late times, providing significant insights into possible extensions of general relativity and bolstering $f(T)$ gravity theory as a robust explanation for cosmic acceleration.

gr-qc↗

arXiv2025

Reconstructing $f(T)$ Gravity From Hubble Parameterization Constraints

In this paper, we have presented the cosmological model of the Universe that represents late time cosmic acceleration in torsion based gravitational theory, the $f(T)$ gravity. A well motivated parametrization for the Hubble parameter has been introduced and the free parameters involved are constrained using the cosmological datasets. With the constrained values of the free parameters, other geometrical parameters such as deceleration parameter, jerk parameter, and snap parameter are analyzed and confronted with the prescribed value of the cosmological observations. In addition, the dynamical parameters are analyzed in some non-linear form of $f(T)$ and the energy conditions are also studied and confirmed with the violation of the strong energy condition. The obtained cosmological model provides late time phantom behavior of the Universe.

gr-qc↗

arXiv2024

Non-linear Analysis Based ECG Classification of Cardiovascular Disorders

Multi-channel ECG-based cardiac disorders detection has an impact on cardiac care and treatment. Limitations of existing methods included variation in ECG waveforms due to the location of electrodes, high non-linearity in the signal, and amplitude measurement in millivolts. The present study reports a non-linear analysis-based methodology that utilizes Recurrence plot visualization. The patterned occurrence of well-defined structures, such as the QRS complex, can be exploited effectively using Recurrence plots. This Recurrence-based method is applied to the publicly available Physikalisch-Technische Bundesanstalt (PTB) dataset from PhysioNet database, where we studied four classes of different cardiac disorders (Myocardial infarction, Bundle branch blocks, Cardiomyopathy, and Dysrhythmia) and healthy controls, achieving an impressive classification accuracy of 100%. Additionally, t-SNE plot visualizations of the latent space embeddings derived from Recurrence plots and Recurrence Quantification Analysis features reveal a clear demarcation between the considered cardiac disorders and healthy individuals, demonstrating the potential of this approach.

cs.CV↗