Related papers: Testing the Cosmic Distance Duality Relation with …
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters. In addition, Bayesian model comparison via the evidence enables direct kernel comparison.…
We investigate deviations from the cosmic distance duality relation adopting model-dependent and -independent approaches using i) a Taylor expansion, ii) a power-law parameterization, iii) a logarithmic correction, iv) a (2;1) Pad\'e…
We calibrate the distance and reconstruct the Hubble diagram of gamma-ray bursts (GRBs) using deep learning. We construct an artificial neural network, which combines the recurrent neural network and Bayesian neural network, and train the…
In this paper we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued…
In this paper, we propose an accurate test of the distance-duality (DD) relation, $\eta=D_{L}(z)(1+z)^{-2}/D_{A}(z)=1$ (where $D_{L}$ and $D_{A}$ are the luminosity distances and angular diameter distances, respectively), with a combination…
The distance duality relation (DDR) between luminosity and angular diameter distances holds if gravity is described by a metric theory and the universe is transparent. Recent cosmological inferences using Type Ia supernovae (SNe~Ia), baryon…
Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL). Unfortunately, due to the kernel optimization process, this often results in losing their Bayesian…
The cosmic distance duality relation is a milestone of cosmology involving the luminosity and angular diameter distances. Any departure of the relation points to new physics or systematic errors in the observations, therefore tests of the…
The distance duality relation (DDR) is valid in Riemannian spacetime. The astronomical data hint that the universe may have certain preferred direction. If the universe is described by anisotropic cosmological models based on Riemannian…
Retinal prostheses restore vision by electrically stimulating surviving neurons, but calibrating perceptual thresholds (i.e., the minimum stimulus intensity required for perception) remains a time-intensive challenge, especially for…
We constrain and compare the $w_{0}w_{a}$CDM dynamical dark energy model and three $f(R)$ modified gravity models using the current cosmological distance measurements, including 112 high-quality localized FRBs, BAO measurements from the…
The distance-duality relation (DDR) between the luminosity distance $D_L$ and the angular diameter distance $D_A$ is viewed as a powerful tool for testing for the opacity of the Universe, being independent of any cosmological model. It was…
In this letter, we propose an improved cosmological model-independent method to measure cosmic curvature, combining the recent measurements of transverse and line-of-sight directions in the baryon acoustic oscillations (BAO) with cosmic…
Gaussian Process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation…
We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent…
Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data-driven method, which requires sufficiently large dataset.…
The 21-cm signal from neutral hydrogen is anticipated to reveal critical insights into the formation of early cosmic structures during the Cosmic Dawn and the subsequent Epoch of Reionization. However, the intrinsic faintness of the signal,…
We apply Gaussian processes (GP) in order to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use available $H(z)$ observations from (i) cosmic chronometers data (CC); (ii) Supernova Type Ia (SN) data from the…
Gaussian Process regression is a kernel method successfully adopted in many real-life applications. Recently, there is a growing interest on extending this method to non-Euclidean input spaces, like the one considered in this paper,…
We propose Bayesian extensions of two nonparametric regression methods which are kernel and mutual $k$-nearest neighbor regression methods. Derived based on Gaussian process models for regression, the extensions provide distributions for…