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We show the kinematic equivalence between cosmological models driven by Dirac-Born-Infeld fields $\phi$ with constant proper velocity of the brane and exponential potential $V=V_0e^{-B\phi}$ and interactive cosmological systems with…
The standard cosmological model currently in force, aka $\Lambda$CDM, has been plagued with a variety of tensions in the last decade or so, which puts it against the wall. At the core of the $\Lambda$CDM we have a rigid cosmological term,…
We parameterize the equation of state of late-time dark energy as $w_{\mathrm{bin}}(z)$, with three redshift bins, characterized by a constant equation of state in each bin. Then, we constrain the parameters of the $w_{\mathrm{bin}}$CDM…
We use a combination of Planck cosmic microwave background (CMB) anisotropy data and non-CMB data that include Pantheon+ type Ia supernovae (SNIa), Hubble parameter [$H(z)$], growth factor ($f\sigma_8$) measurements, and a collection of…
In this paper, we assemble a catalog of 118 strong gravitational lensing systems from SLACS, BELLS, LSD and SL2S surveys and use them to constrain the cosmic equation of state. In particular we consider two cases of dark energy…
In this investigation, we perform an observational statistical analysis in the theory of $ f(R, L_m) $ gravity. The proposed theoretical model is based on the Ricci scalar's non-linear contribution. We use a distinct parameterization for…
Interacting dark energy models have been suggested as alternatives to the standard cosmological model, $\Lambda$CDM. We focus on a phenomenologically interesting class of dark scattering models that is characterised by pure momentum…
We explore the observational adequacy of a class of Unified Dark Energy/Matter (UDE/M) models with a fast transition. Our constraints are set using a combination of geometric probes, some low redshift ones, and some high redshift ones (CMB…
We study structure formation in alternative cosmological models constrained by background observations, including $\Lambda$CDM, wCDM, the Chevallier-Polarski-Linder parametrisation and a flexible Chebyshev expansion of the dark energy…
We present a comprehensive analysis of the cosmological implications of the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) Lyman-$\alpha$ forest baryon acoustic oscillation (BAO) measurements, combined with DESI DR2 galaxy…
We perform a comparison of the WMAP measurements with the predictions of quintessence cosmological models of dark energy. We consider a wide range of quintessence models, including: a constant equation-of-state; a simply-parametrized,…
The nature of dark energy is one of the big puzzling issues in cosmology. While $\Lambda$CDM provides a good fit to the observational data, evolving dark energy scenarios, such as the CPL parametrization, offer a compelling alternative. In…
There are a number of mathematical theorems in the literature on the dynamics of cosmological models with accelerated expansion driven by a positive cosmological constant $\Lambda$ or a nonlinear scalar field with potential $V$…
Recent baryon acoustic oscillation (BAO) measurements released by DESI, when combined with cosmic microwave background (CMB) data and type Ia supernova (SN) data, suggest a significant preference for dynamical dark energy (DDE) that…
In this study, we have demonstrated the expansion history of an axially symmetric Bianchi type-I model of the universe. Our model as of now presents an accelerating universe, which had been in the decelerating phase in the past. Roles of…
We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and…
Scalar-tensor theories have shown great potential in inducing tailored modifications compared to cosmic evolution in the $\Lambda$CDM model. We reconsider quintessence models in this work in the context of three driving potentials. We…
The cosmic microwave background power spectra are a primary window into the early universe. However, achieving interpretable, likelihood-compatible compression and fast inference under weak model assumptions remains challenging. We propose…
Machine learning models have emerged as powerful tools in physics and engineering. Although flexible, a fundamental challenge remains on how to connect new machine learning models with known physics. In this work, we present an autoencoder…
A novel sequential inferential method for Bayesian dynamic generalised linear models is presented, addressing both univariate and multivariate $k$-parametric exponential families. It efficiently handles diverse responses, including…