相关论文: Solving the $H_{0}$ tension in $f(T)$ Gravity thro…

We use supernovae measurements, calibrated by the local determination of the Hubble constant $H_0$ by SH0ES, to interpolate the distance-redshift relation using Gaussian process regression. We then predict, independent of the cosmological…

The classical approach to non-linear regression in physics, is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then, using non-linear fitting algorithms,…

To reconcile the current tensions between high and low redshift observations, we perform the first constraints on the Finslerian cosmological models including the effective dark matter and dark energy components. We find that all the four…

This work examines the Hubble constant (\(H_0\)) tension within the frameworks of perturbed \(f(R)\) gravity and perturbed \(f(R)\) gravity coupled with neutrinos, using lastest observational data. The datasets incorporate the Cosmic…

\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while…

We revisit the local distance ladder measurement of the Hubble constant in models where gravity is modified by a fifth force, an additional long-range interaction. In many such theories the force is screened; suppressed in dense…

The time delay in gravitational lenses can be used to derive the Hubble constant in a relatively simple way. The results of this method are less dependent on astrophysical assumptions than in many other methods. The most important…

Machine learning methods for solving the equations of dynamical mean-field theory are developed. The method is demonstrated on the three dimensional Hubbard model. The key technical issues are defining a mapping of an input function to an…

This paper examines the late-time accelerating Universe and the formation of large-scale structures within the modified symmetric teleparallel gravity framework, specifically using the $f(Q)$-gravity model, in light of recent cosmological…

We present a comprehensive Bayesian analysis of the Hubble constant within the framework of Physics-Informed Neural Networks (PINNs), focusing on the standard $\Lambda$CDM model and its dynamical dark energy extensions described by the…

The standard model of modern cosmology might be cracked by the recent persistent hot debate on the Hubble-constant ($H_0$) tension, which manifests itself as the sound-horizon ($r_s$) tension or absolute-magnitude ($M_B$) tension if deeming…

A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesheli et al., 2018]…

Large language models (LLMs) exhibit probabilistic output characteristics, yet conventional evaluation frameworks rely on deterministic scalar metrics. This study introduces a Bayesian approach for LLM capability assessment that integrates…

This paper examines free-form modeling of gravitational lenses using Bayesian ensembles of pixelated mass maps. The priors and algorithms from previous work are clarified and significant technical improvements are made. Lens reconstruction…

We probe four cosmological models which, potentially, can solve the Hubble tension according to the dark energy equation of state. In this context, we demonstrate that the Einstein Telescope is capable of achieving a relative accuracy below…

We study scalar cosmological perturbations in $f(R, T)$ modified gravity theories being $T$ the trace of the energy-momentum tensor. We provide detailed equations for the matter energy density contrast. We solve then numerically to promote…

Deep directed generative models have attracted much attention recently due to their expressive representation power and the ability of ancestral sampling. One major difficulty of learning directed models with many latent variables is the…

Language Models (LMs) exhibit two distinct mechanisms for knowledge acquisition: in-weights learning (i.e., encoding information within the model weights) and in-context learning (ICL). Although these two modes offer complementary…

We present Gravity.jl, a new software for the modeling of gravitational lens systems. Gravity.jl is written in the Julia programming language, and is designed to be fast, accurate, and flexible. It can be used to model gravitational lens…

One challenge in strong gravitational lensing cosmography is the measurement of time delays between multiple lensed images, which are essential for constraining the Hubble constant (\(H_0\)). In this study, we investigate how assumptions…