相关论文: Constraints on Cosmic Opacity from Bayesian Machin…
In this paper, two inhomogeneous single fluid models for the Universe, which are able to naturally solve the $H_{0}$ tension problem, are discussed. The analysis is based on a Bayesian Machine Learning approach that uses a generative…
The $H_{0}$ tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic)…
Constraints on a dark energy dominated Universe are obtained from an interplay between Bayesian Machine Learning and string Swampland criteria. The approach here differs from previous studies, since in the generative process Swampland…
In this work, two different models, one with cosmological constant $\Lambda$, and baryonic and dark matter (with $\omega_{dm} \neq 0$), and the other with an $X$ dark energy (with $\omega_{de} \neq -1$), and baryonic and dark matter (with…
We present our study on cosmic opacity, which relates to changes in photon number as photons travel from the source to the observer. Cosmic opacity may be caused by absorption/scattering due to matter in the universe, or by extragalactic…
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…
Probing the evolution of the universe at high redshifts with standard candles is a powerful way to discriminate dark energy models, where an open question nowadays is whether this component is constant or evolves with time. One possible…
Equations of cosmic dynamics for a model of the opaque Universe are derived and tested on supernovae (SNe) Ia observations. The model predicts a cyclic expansion/contraction evolution of the Universe within a limited range of scale factors.…
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such model might replace a proposed non-gravitational interaction between dark energy and dark matter, and…
Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on…
The nature of dark matter and dark energy are among the central questions in cosmology. Strong gravitational lenses with multiple source planes provide a geometric probe of cosmology: the ratio of deflection angles at different redshifts…
While Bayesian model selection is a useful tool to discriminate between competing cosmological models, it only gives a relative rather than an absolute measure of how good a model is. Bayesian doubt introduces an unknown benchmark model…
Bayesian Machine Learning~(BML) and strong lensing time delay~(SLTD) techniques are used in order to tackle the $H_{0}$ tension in $f(T)$ gravity. The power of BML relies on employing a model-based generative process which already plays an…
Recent astronomical observations of SNIa, CMB, as well as BAO in the Sloan Digital Sky Survey, suggest that the current Universe has entered a stage of an accelerated expansion with the transition redshift at $z \simeq 0.5$. While the…
We use two model-independent methods to constrain the curvature of the universe. In the first method, we study the evolution of the curvature parameter ($\Omega_k^0$) with redshift by using the observations of the Hubble parameter and…
Conservation of the phase-space density of photons plus Lorentz invariance requires that the cosmological luminosity distance be larger than the angular diameter distance by a factor of $(1+z)^2$, where $z$ is the redshift. Because this is…
We present results exploring the role that probabilistic deep learning models can play in cosmology from large scale astronomical surveys through estimating the distances to galaxies (redshifts) from photometry. Due to the massive scale of…
Cosmic opacity may vary spatially due to the inhomogeneous distribution of dust, its grain properties, and the efficiency of photon attenuation. In this work, we present a model independent method to investigate the variation of cosmic…
In this paper, we present a cosmological model-independent test for the cosmic opacity at high redshifts ($z\sim5$). We achieve this with the opacity-dependent luminosity distances derived from nonlinear relation between X-ray and UV…
Bayesian model selection methods provide a self-consistent probabilistic framework to test the validity of competing scenarios given a set of data. We present a case study application to strong gravitational lens parametric models. Our goal…