Related papers: Analytic auto-differentiable $\Lambda$CDM cosmogra…
Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators…
We propose a novel approach for parameterizing the luminosity distance, based on the use of rational "Pad\'e" approximations. This new technique extends standard Taylor treatments, overcoming possible convergence issues at high redshifts…
The Planck satellite, along with several ground based telescopes, have mapped the cosmic microwave background (CMB) at sufficient resolution and signal-to-noise so as to allow a detection of the subtle distortions due to the gravitational…
In this study, we introduce a novel analytical Gaussian Process (GP) cosmography methodology, leveraging the differentiable properties of GPs to derive key cosmological quantities analytically. Our approach combines cosmic chronometer (CC)…
In this paper, by considering an absorption probability independent of photon wavelength, we show that current type Ia supernovae (SNe Ia) and gamma ray burst (GRBs) observations plus high-redshift measurements of the cosmic microwave…
We present an analytical approximation formula for the luminosity distance in spatially flat cosmologies with dust and a cosmological constant. Apart from the overall factor, the effect of non-zero cosmological constant in our formula is…
We present jax-cosmo, a library for automatically differentiable cosmological theory calculations. It uses the JAX library, which has created a new coding ecosystem, especially in probabilistic programming. As well as batch acceleration,…
Cosmological distances are fundamental observables in cosmology. The luminosity ($D_L$), angular diameter ($D_A$) and gravitational wave ($D_{\rm GW}$) distances are all trivially related in General Relativity assuming no significant…
We investigate higher order symplectic integration strategies within Bayesian cosmic density field reconstruction methods. In particular, we study the fourth-order discretisation of Hamiltonian equations of motion (EoM). This is achieved by…
We study exact cosmological solutions in $f(Q)$ gravity formulated beyond the coincident gauge, focusing on the non-coincident connection branch $\Gamma_B$. Using a minisuperspace approach, the field equations are recast into an equivalent…
Cosmography has been extensively utilized to constrain the kinematic state of the Universe using measured distances. In this work, we propose a new method to reconstruct coupling theories using the first kind of Chebyshev polynomial for two…
The most recent cosmological observations indicate that the present universe is flat and vacuum dominated. In such a universe, the distance measurements are always difficult and involve numerical computations. In this paper, it is shown…
The cosmic distance ladder is the succession of techniques by which it is possible to determine distances to astronomical objects. Here, we present a new method to build the cosmic distance ladder, going from local astrophysical…
In this study, we apply the Analytical method of Blind Separation (ABS) of the cosmic microwave background (CMB) from foregrounds to estimate the CMB temperature power spectrum from multi-frequency microwave maps. We test the robustness of…
Recently, cosmography emerged as a valuable tool to effectively describe the vast amount of astrophysical observations without relying on a specific cosmological model. Its model-independent nature ensures a faithful representation of data,…
In this paper, an analytic approximation method for highly nonlinear equations, namely the homotopy analysis method (HAM), is employed to solve some backward stochastic differential equations (BSDEs) and forward-backward stochastic…
Although many exoplanets have been indirectly detected over the last years, direct imaging of them with ground-based telescopes remains challenging. In the presence of atmospheric fluctuations, it is ambitious to resolve the high brightness…
Approximate Bayesian computation (ABC) using a sequential Monte Carlo method provides a comprehensive platform for parameter estimation, model selection and sensitivity analysis in differential equations. However, this method, like other…
Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints. For complex datasets it is hard to avoid making some arbitrary choices when defining a distance…
Weak gravitational lensing is one of the few direct methods to map the dark-matter distribution on large scales in the Universe, and to estimate cosmological parameters. We study a Bayesian inference problem where the data covariance…