Related papers: ECoPANN: A Framework for Estimating Cosmological P…
We present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time…
In the current study, we present the observational data constraints on the parameters space for an anisotropic cosmological model of Bianchi I type spacetime in general relativity (GR). For the analysis, we consider observational datasets…
The Cosmic Microwave Background (CMB) is an abundant source of cosmological information. However, this information is encoded in non-trivial ways in a signal that is difficult to observe. The resulting challenges in extracting this…
Data analysis from upcoming large galaxy redshift surveys, such as Euclid and DESI will significantly improve constraints on cosmological parameters. To optimally extract the information from these galaxy surveys, it is important to control…
Context: The cosmic microwave background (CMB) spectrum probes physical processes and astrophysical phenomena occurring at various epochs of the Universe evolution. Current and future CMB absolute temperature experiments are aimed to the…
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characterize the underlying physical system -- our Universe. Modern…
We present a new method for calculating linear cosmic microwave background (CMB) anisotropy spectra based on integration over sources along the photon past light cone. In this approach the temperature anisotropy is written as a time…
In cosmology, the analysis of observational evidence is very important to test theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and are recently being…
The possibility of determining cosmological parameters on the basis of a wide set of observational data including the Abell-ACO cluster power spectrum and mass function, peculiar velocities of galaxies, the distribution of Ly-$\alpha$…
The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics like stars, star clusters, and the formation of galaxies. In this paper, we present a computational scheme to…
We present a novel method to significantly speed up cosmological parameter sampling. The method relies on constructing an interpolation of the CMB-log-likelihood based on sparse grids, which is used as a shortcut for the…
We describe the Bayesian-based signal-to-noise eigenmode method for cosmological parameter estimation, show how it can be used to optimally compress large CMB anisotropy data sets to manageable sizes, and apply it to the DMR 4-year, South…
Several cosmological measurements have attained significant levels of maturity and accuracy over the last decade. Continuing this trend, future observations promise measurements of the statistics of the cosmic mass distribution at an…
The cross-correlation between the cosmic microwave background (CMB) fields and matter tracers carries important cosmological information. In this paper, we forecast by a signal-to-noise ratio analysis the information contained in the…
We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave radiation background. We propose the…
We introduce a new method to propagate uncertainties in the beam shapes used to measure the cosmic microwave background to cosmological parameters determined from those measurements. The method, which we call Markov Chain Beam…
The discovery of cosmic microwave background (CMB) was a paradigm shift in the study and fundamental understanding of the early universe and also the Big Bang phenomenon. Cosmic microwave background is one of the richest and intriguing…
We study the ability of future CMB anisotropy experiments and redshift surveys to constrain a thirteen-dimensional parameterization of the adiabatic cold dark matter model. Each alone is unable to determine all parameters to high accuracy.…
The future 21 cm intensity mapping observations constitute a promising way to trace the matter distribution of the Universe and probe cosmology. Here we assess its capability for cosmological constraints using as a case study the BINGO…
Easy Parameter Inference in Cosmology (EPIC) is another Markov Chain Monte Carlo (MCMC) sampler for Cosmology. It is implemented in Python and provides Bayesian parameter inference and model comparison based on the Bayesian evidence. The…