Related papers: Observational constraints using Bayesian statistic…
The higher-curvature gravity with boundary terms i.e the $f(Q)$ theories, grounded on non-metricity as a fundamental geometric quantity, exhibit remarkable efficacy in portraying late-time universe phenomena. The aim is to delineate…
In this work, we investigate the cosmological consequences of an effective $f(Q)$ model emerging from the more general $f(Q,T)$ gravity theory under the special equation-of-state condition $\rho + p = 0$. Under this limit, the field…
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…
This paper addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring…
We investigate the cosmological viability of symmetric teleparallel gravity, specifically the $f(Q)$ gravity model with a power-law form $f(Q) = \alpha Q^n$, in combination with two widely used dark energy parameterizations: Chevallier…
The $f(Q,T)$ cosmological model has emerged as a promising framework for understanding various aspects of cosmic evolution. In this study, we focused on obtaining the constraints of the free parameters in the non-linear form of…
In this paper, we considered the study of Friedmann-Robertson-Walker (FRW) model in the framework of $f(Q,T)$ gravity, an extension of symmetric teleparallel gravity, recently defined by Y. Xu et al. \cite{Xu}. The non-linear model…
In this letter, we investigate cosmology within the framework of modified $f(Q, L_m)$ gravity using the non-linear model $f(Q, L_m) = -Q + \alpha L_m^n + \beta$, where $\alpha$, $\beta$, and $n$ are free parameters. The modified Friedmann…
We present CosmicANNEstimator (Cosmological Parameters Artificial Neural Network Estimator), a machine learning approach for constraining cosmological parameters within the Lambda Cold Dark Matter ($\Lambda$CDM) framework. Our methodology…
The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate…
Considering a well-motivated $f(R)$ modified-gravity model, in which an exponential function of the curvature is included, in this paper we implement a statistical data analysis to set constraints on the parameters of the model, taking into…
This work examines the cosmological implications of two functional forms of $f(R,T) = R + \alpha T^n$ gravity: for two different value of $n $ where $n=1$ and $n\neq 1$, and $\alpha$ and $n$ are free parameters. The modified Friedmann…
In this paper, the dynamical behavior of the accelerated expansion of the universe is discussed within the framework of $f(T)$ gravity, considering power law functional form of $ f(T)=\alpha (-T)^{n}$. Two distinct redshift-dependent…
In this article, we have investigated the role of bulk viscosity to study the accelerated expansion of the universe in the framework of modified $f(Q)$ gravity. The gravitational action in this modified gravity theory has the form $f(Q)$,…
In this paper, we have studied the late-time accelerating expansion of the Universe using the matter-geometry coupled $ f(Q, T) $ gravity model, where $ Q $ is the non-metricity scalar and $ T $ represents the trace of the energy-momentum…
Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The…
In this study, we investigate a Palatini $f(R)$ gravity model featuring a quadratic term correction, aligning it with the most recent expansion rate data, with a particular focus on the latest SNIa and BAO data. Our analysis employs CC data…
This study investigates the cosmological dynamics of an accelerating universe within the framework of teleparallel gravity using an exponential f(T) functional form. To obtain exact cosmological solutions, a hybrid scale factor is employed…
We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…
Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the cosmic expansion history. However, the calibration of their luminosity distances is not an easy task in comparison to Type Ia Supernovae (SNeIa). To calibrate these…