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

In this paper, we investigate the stability of the flat FLRW metric in $f(T)$ gravity. This is achieved by analysing the small perturbations, $\delta$ about the Hubble parameter and the matter energy density, $\delta_\text{m}$. We find that…

We use galaxy-galaxy lensing data to test General Relativity and $f(T)$ gravity at galaxies scales. We consider an exact spherically symmetric solution of $f(T)$ theory which is obtained from an approximate quadratic correction, and thus it…

The $\Lambda$CDM model provides an excellent fit to the CMB data. However, a statistically significant tension emerges when its determination of the Hubble constant $H_0$ is compared to the local distance-redshift measurements. The…

This study explored the behavior of the $f(T, \mathcal{T})$ cosmological model with the use of various data set combinations. We also compared the results for this model between the Pantheon+ (without SH0ES) and the Pantheon+\&SH0ES (with…

In this paper, we have presented the cosmological model of the Universe that represents late time cosmic acceleration in torsion based gravitational theory, the $f(T)$ gravity. A well motivated parametrization for the Hubble parameter has…

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…

Surrogate modeling is an essential data-driven technique for quantifying relationships between input variables and system responses in manufacturing and engineering systems. Two major challenges limit its effectiveness: (1) large data…

Restricted Boltzmann Machines are key tools in Machine Learning and are described by the energy function of bipartite spin-glasses. From a statistical mechanical perspective, they share the same Gibbs measure of Hopfield networks for…

A simple and minimal extension of the standard cosmological $\Lambda$CDM model in which dark matter experiences an additional long-range scalar interaction is demonstrated to alleviate the long lasting Hubble-tension while letting…

Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may…

In this paper, we describe a procedure for modelling strong lensing galaxy clusters with parametric methods, and to rank models quantitatively using the Bayesian evidence. We use a publicly available Markov chain Monte-Carlo (MCMC) sampler…

Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance improvement. The advent of multimodal data allows tasks to be referenced by multiple indices. High-order tensors are capable of providing efficient…

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…

We consider a machine learning algorithm to detect and identify strong gravitational lenses on sky images. First, we simulate different artificial but very close to reality images of galaxies, stars and strong lenses, using six different…

Based on recent advancements in using machine learning for classical density functional theory for systems with one-dimensional, planar inhomogeneities, we propose a machine learning model for application in two dimensions (2D) akin to…

A family of simple and minimal extensions of the standard cosmological $\Lambda$CDM model in which dark matter experiences an additional long-range scalar interaction is demonstrated to alleviate the long lasting Hubble-tension while…

We consider the effective field theory formulation of torsional gravity in a cosmological framework to alter the background evolution. Then we use the latest $H_0$ measurement from the SH0ES Team as well as observational Hubble data from…

The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that…

The simplest cosmological model ($\Lambda$CDM) is well-known to suffer from the Hubble tension, namely an almost $5 \sigma$ discrepancy between the (model-based) early-time determination of the Hubble constant $H_0$ and its late-time (and…

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely…