相关论文: Machine Learning Cosmic Expansion History
Cosmic microwave background measurements have pushed to higher resolution, lower noise, and more sky coverage. These data enable a unique test of the early universe's expansion rate and constituents such as effective number of relativistic…
The current tension between early- and late-Universe measurements of the Hubble constant ($H_0$), along with the still elusive nature of dark matter and dark energy, calls for model-independent probes of the Universe's expansion history.…
The interpretation of cosmological observables requires the use of increasingly sophisticated theoretical models. Since these models are becoming computationally very expensive and display non-trivial uncertainties, the use of standard…
A new parametrization of the Hubble parameter is proposed to explore the issue of the cosmological landscape. The constraints on model parameters are derived through the Markov Chain Monte Carlo (MCMC) method by employing a comprehensive…
We investigate the prospects for determining the accelerating history of the Universe from upcoming measurements of the expansion rate $H(z)$. In our analyses, we use Monte Carlo simulations based on $w$CDM models to generate samples with…
An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the…
The cosmic acceleration observed in the expansion of the Universe has sparked extensive research into the nature of dark energy, which is known to constitute approximately 70\% of the Universe's energy content. In this study, we explore two…
According to the separate universe conjecture, spherically symmetric sub-regions in an isotropic universe behave like mini-universes with their own cosmological parameters. This is an excellent approximation in both Newtonian and general…
Non-trivial spatial topology of the Universe may give rise to potentially measurable signatures in the cosmic microwave background. We explore different machine learning approaches to classify harmonic-space realizations of the microwave…
Cosmography, as an integral branch of cosmology, strives to characterize the Universe without relying on pre-determined cosmological models. This model-independent approach utilizes Taylor series expansions around the current epoch,…
The evolution of the Hubble parameter $H(z)$ with redshift $z$ is estimated from the Pantheon+ data of Type Ia supernovae, for the $\Lambda$CDM model and the three special cases of the eternal coasting (EC) cosmological model with three…
The cosmographic approach is gaining considerable interest as a model-independent technique able to describe the late expansion of the universe. Indeed, given only the observational assumption of the cosmological principle, it allows to…
Given a class of dark energy models, constraints from one set of cosmic acceleration observables make predictions for other observables. Here we present the allowed ranges for the expansion rate H(z), distances D(z), and the linear growth…
Bayesian (Probabilistic) Machine Learning is used to probe the opacity of the Universe. It relies on a generative process where the model is the key object to generate the data involving the unknown parameters of the model, our prior…
We present a simple and promising new method to measure the expansion rate and the geometry of the universe that combines observations related to the time delays between the multiple images of time-varying sources, strongly lensed by galaxy…
Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent…
We model the expansion history of the Universe as a Gaussian Process and find constraints on the dark energy density and its low-redshift evolution using distances inferred from the Luminous Red Galaxy (LRG) and Lyman-alpha (Ly$\alpha$)…
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well…
The possibility of having an extension of the cosmological standard model with a Hubble expansion rate $H$ constrained to a finite interval is considered. Two periods of accelerated expansion arise naturally when the Hubble expansion rate…
In this paper, we show that the expansion history of the Universe in power-law cosmology essentially depends on two crucial parameters, namely the Hubble constant $H_{0}$ and deceleration parameter $q$. We find the constraints on these…