article · International Journal of Geometric Methods in Modern Physics
This research examines the accelerating expansion of the universe within a modified theory of gravity known as f(Q) cosmology, which relies on a non-metricity scalar to describe gravitational interactions. A specific mathematical formula parametrising the deceleration parameter is tested against multiple astronomical observations. These observational datasets include fifty-seven measurements of Hubble parameter data, over one thousand data points from the Pantheon supernova compilation, ten measurements from baryon acoustic oscillations, and cosmic microwave background shift parameters from the Planck 2018 mission. Model validation is performed through the Om diagnostic and the assessment of standard energy conditions. Finally, statistical evaluation using model selection criteria assesses how strongly the proposed model aligns with the standard cosmological paradigm.
Understanding how the universe accelerates helps physicists refine the fundamental laws governing gravity and cosmic history. Testing modified gravity theories against vast sets of observational data provides deeper insight into whether alternate mathematical descriptions can successfully replicate or improve upon accepted models of the cosmos.
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In this paper, we investigate the accelerated expansion of the Universe in the context of [Formula: see text] modified theory of gravity, where [Formula: see text] is a non-metricity scalar which characterizes the gravitational interaction by using parametrization of the deceleration parameter [Formula: see text] with [Formula: see text], where [Formula: see text] and [Formula: see text] are free parameters constrained by the 57 points of [Formula: see text] datasets, 1048 points of Pantheon, 10 points from Baryon Acoustic Oscillations (BAO) datasets and the shift parameters from Planck 2018 of Cosmic Microwave Background (CMB). In the purpose of validating our model, we proceed by the Om diagnostic and the energy conditions. Later we discussed how our model statistically supports [Formula: see text]CDM using [Formula: see text] criterion analysis.
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DOI: 10.1142/s0219887823501529
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