相关论文: Learning Theory Informed Priors for Bayesian Infer…

One of the most appealing approaches to ease the Hubble tension is the inclusion of an early dark energy (EDE) component that adds energy to the Universe in a narrow redshift window around the time of recombination and dilutes faster than…

Identifying governing partial differential equations (PDEs) from noisy spatiotemporal data remains challenging due to differentiation-induced noise amplification and ambiguity from overcomplete libraries. We propose a prior-informed…

A dark energy-like component in the early universe, known as early dark energy (EDE), is a proposed solution to the Hubble tension. Currently, there is no consensus in the literature as to whether EDE can simultaneously solve the Hubble…

We test the $n$=3 Ultralight Axion-like model of Early Dark Energy (EDE) with the observationsof the $EB$ mode of the cosmic microwave background (CMB) radiation, and local expansion rate measurements. Our results show that the shape of the…

We revisit the theoretical priors used for inferring Dark Energy (DE) parameters. Any DE model must have some form of a tracker mechanism such that it behaved as matter or radiation in the past. Otherwise, the model is fine-tuned. We…

Current cosmological data exhibit a tension between inferences of the Hubble constant, $H_0$, derived from early and late-universe measurements. One proposed solution is to introduce a new component in the early universe, which initially…

The Hubble tension persists as a challenge in cosmology. Even early dark energy (EDE) models, initially considered the most promising for alleviating the Hubble tension, fall short of addressing the issue without exacerbating other…

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization contains…

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 paper we fit two models of Early Dark Energy (EDE) (an increase in the expansion rate before recombination) to the combination of Atacama Cosmology Telescope (ACT) measurements of the Cosmic Microwave Background (CMB) with data from…

Cosmic birefringence and the Hubble tension represent compelling challenges to the standard $\Lambda$CDM model. The early dark energy (EDE) model with potentials $V(\phi) \propto [1-\cos(\phi/f)]^n$ offer a unified framework to address both…

The Hubble constant tension problem is analysed in the framework of a class of modified gravity, the so-called $F(R)$ gravity. To do so, we explore two models: an exponential and a power-law $F(R)$ gravities, which includes an early dark…

Physics-informed deep learning have recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such…

This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples from the joint distribution of inferred variables and measurements are assumed available, while…

The prevailing data-driven machine learning has been plagued by the absence of physics knowledge and the scarcity of data. We implement the physics-model informed prior into Bayesian machine learning to evaluate the energy dependence of…

This work examines an early dark energy (EDE) scenario in the context of $F(R)$ gravity. EDE is introduced to alleviate the Hubble tension by temporarily injecting approximately $10\%$ of the energy fraction around the matter-radiation…

We consider an Early Dark Energy (EDE) cosmological model, and perform an analysis which takes into account both background and perturbation effects via the parameters $c^{2}_{\rm eff}$ and $c^{2}_{\rm vis}$, representing effective sound…

We evaluate the effectiveness of Early Dark Energy (EDE) in addressing the Hubble tension using data from the completed eBOSS survey, focusing on luminous red galaxies (LRGs), quasars (QSOs), and emission line galaxies (ELGs). We perform…

Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets. While…

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…