A New Om(z) Diagnostic of Dark Energy in General Relativity Theory
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In this paper, we propose a new parametrization of dark energy based on the $Om(z)$ diagnostic tool behavior. For this purpose, we investigate a functional form of the $Om(z)$ that predicts the popular dark energy dynamical models, namely phantom and quintessence. We also found the famous cosmological constant for specified values of the model's parameters. We employed the Markov Chain Monte Carlo approach to constrain the cosmological model using Hubble, Pantheon samples, and BAO datasets. Finally, we used observational constraints to investigate the characteristics of dark energy evolution and compare our findings to cosmological predictions.
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Latent-Space Gaussian Processes for Dark-Energy Reconstruction from Observational \(H(z)\) Data
Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high...
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