Modeling Transit Dark Energy in $f(R, L_m)$-gravity
Anirudh Pradhan, Dinesh Chandra Maurya, Gopikant K. Goswami,, Aroonkumar Beesham

TL;DR
This paper develops a cosmological model within $f(R, L_m)$-gravity that fits observational data well, describing the universe's transition from deceleration to acceleration and estimating key cosmological parameters.
Contribution
It introduces a transit dark energy model in $f(R, L_m)$-gravity constrained by multiple observational datasets, providing new insights into the universe's acceleration transition.
Findings
Model fits observational data effectively
Cosmographic coefficients estimated accurately
Universe's transition from deceleration to acceleration confirmed
Abstract
This research paper deals with a transit dark energy cosmological model in -gravity with observational constraints. For this, we consider a flat FLRW space-time and have taken a cosmological cosntant-like parameter in our field equations. The model has two energy parameters~ , which govern the mechanism of the universe, in particular its present accelerated phase. To make the model cope with the present observational scenario, we consider three types of observational data set: Hubble parameter data set, SNe Ia data sets of distance modulus and apparent magnitude, and datasets of SNe Ia Bined compilation in the redshift . We have approximated the present values of the energy parameters by applying and -test in the observational and theoretical values of Hubble, distance modulus, and…
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Taxonomy
TopicsCosmology and Gravitation Theories · Geophysics and Gravity Measurements · Computational Physics and Python Applications
