Related papers: Low redshift observational constraints on dark ene…
We show that the impact of energy injection by dark matter annihilation on the cosmic microwave background power spectra can be apprehended via a residual likelihood map. By resorting to convolutional neural networks that can fully discover…
We investigate a dynamical reconstruction of the dark energy equation of state parameter by assuming that it satisfies a law of motion described by an autonomous second-order differential equation, with the limit of the cosmological…
The expansion rate of the Universe changes with time, initially slowing (decelerating) when the universe was matter dominated, because of the mutual gravitational attraction of all the matter in it, and more recently speeding up…
We introduce a Markov Chain Monte Carlo simulation and data analysis package that extends the CMBEASY software. We have taken special care in implementing an adaptive step algorithm for the Markov Chain Monte Carlo in order to improve…
We discuss variants of Cold Dark Matter (CDM) dominated cosmological models that give good agreement with a range of observations. We consider models with hot dark matter, tilt, $\Omega < 1$, or a cosmological constant. We also discuss the…
In this work, we have considered a model independent approach to study the nature of the late time cosmic acceleration. We have used the Pade approximation to parametrize the comoving distance. Consequently, from this comoving distance, we…
We present a novel deep learning-based algorithm to accelerate - through the use of Artificial Neural Networks (ANNs) - the convergence of Algebraic Multigrid (AMG) methods for the iterative solution of the linear systems of equations…
We present current and future constraints on equations of state for dark sector perturbations. The equations of state considered are those corresponding to a generalized scalar field model and time-diffeomorphism invariant $L(g)$ theories…
The present work deals with kinematical models of latetime cosmology. It is based on purely phenomenological assumption about the deceleration parameter. The models are confronted to observational data sets of type Ia supernovae distance…
We use Gaussian processes in combination with MCMC method to place constraints on cosmological parameters of three dark energy models including flat and curved FRW and Bianchi type I spacetimes. To do so, we use recently compiled 36…
We consider nonlinear redshift-dependent equation of state parameters as dark energy models in a spatially flat Friedmann-Lema\^itre-Robertson-Walker universe. To depict the expansion history of the universe in such cosmological scenarios,…
With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the…
Machine learning is currently a trending topic in various science and engineering disciplines, and the field of geophysics is no exception. With the advent of powerful computers, it is now possible to train the machine to learn complex…
Thawing quintessence scalar field models with the various potential forms to explain the late-time cosmic acceleration are compared to the {\Lambda}CDM model in detail by analyzing cosmological parameters with a set of observational data…
Machine learning (ML) methods have become popular for parameter inference in cosmology, although their reliance on specific training data can cause difficulties when applied across different data sets. By reproducing and testing networks…
A regularized artificial neural network (RANN) is proposed for interval-valued data prediction. The ANN model is selected due to its powerful capability in fitting linear and nonlinear functions. To meet mathematical coherence requirement…
We investigate constraints on some key cosmological parameters by confronting metastable dark energy models with different combinations of the most recent cosmological observations. Along with the standard $\Lambda$CDM model, two…
$K$-NN classifier is one of the most famous classification algorithms, whose performance is crucially dependent on the distance metric. When we consider the distance metric as a parameter of $K$-NN, learning an appropriate distance metric…
Given the persistence of various tensions in the "Cosmic Concordance" -- such as the "Hubble Tension", and possible departures from LambdaCDM time evolution -- seen from combinations of complementary data sets (e.g., Cosmic Microwave…
We investigate the constraints on cosmological parameters especially for EoS of dark energy, inflationary parameters, neutrino mass and curvature of universe using simulated Planck data. Firstly we determine cosmological parameters with…