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We use the newly released 182 Type Ia supernova data combined with the third-year Wilkinson Microwave Anisotropic Probe data (WMAP3) and large scale structure (LSS) information including SDSS and 2dFGRS to constrain the dark energy equation…

天体物理学 · 物理学 2008-11-26 Gong-Bo Zhao , Jun-Qing Xia , Hong Li , Charling Tao , Jean-Marc Virey , Zong-Hong Zhu , Xinmin Zhang

Recently, the vacuum energy of the QCD ghost in a time-dependent background is proposed as a kind of dark energy candidate to explain the acceleration of the universe. In this model, the energy density of the dark energy is proportional to…

宇宙学与河外天体物理 · 物理学 2013-02-22 Chao-Jun Feng , Xin-Zhou Li , Xian-Yong Shen

Decaying Dark Energy models modify the background evolution of the most common observables, such as the Hubble function, the luminosity distance and the Cosmic Microwave Background temperature-redshift scaling relation. We use the most…

宇宙学与河外天体物理 · 物理学 2018-09-05 Ivan de Martino

Two new one-parameter tracking behavior dark energy representations $\omega=\omega_0/(1+z)$ and $\omega=\omega_0 e^{z/(1+z)}/(1+z)$ are used to probe the geometry of the Universe and the property of dark energy. The combined type Ia…

天体物理学 · 物理学 2009-11-10 Yungui Gong , Yuan-Zhong Zhang

In this paper, we combine the the latest observational data, including the WMAP five-year data (WMAP5), the baryon acoustic oscillations (BAO) and type Ia supernovae (SN) "union" compilation, and use the Markov Chain Monte Carlo method to…

宇宙学与河外天体物理 · 物理学 2010-05-11 Hong Li , Jun-Qing Xia

In this paper, we report the results of constraining the holographic dark energy model with spatial curvature and massive neutrinos, based on a Markov Chain Monte Carlo global fit technique. The cosmic observational data include the full…

宇宙学与河外天体物理 · 物理学 2013-02-21 Yun-He Li , Shuang Wang , Xiao-Dong Li , Xin Zhang

The coupling between the dark components provides a new approach to mitigate the coincidence problem of cosmological standard model. In this paper, dark energy is treated as a fluid with a constant equation of state, whose coupling with…

宇宙学与河外天体物理 · 物理学 2014-08-18 Weiqiang Yang , Lixin Xu

There are many kinds of models which describe the dynamics of dark energy (DE). Among all we adopt an equation of state (EoS) which varies as a function of time. We adopt Markov Chain Monte Carlo method to constrain the five parameters of…

宇宙学与河外天体物理 · 物理学 2016-11-15 R. Ichimasa , E. P. B. A. Thushari , M. Hashimoto

Cosmological measurements suggest that our universe contains a dark energy component. In order to study the dark energy evolution, we constrain a parameterized dark energy equation of state $w(z)=w_0 + w_1 \frac{z}{1+z}$ using the recent…

天体物理学 · 物理学 2008-11-26 Chengwu Zhang , Lixin Xu , Baorong Chang , Hongya Liu

We investigate observational constraints on the curvature of the universe not restricting ourselves to a cosmological constant as dark energy, in particular allowing a dark energy equation of state to evolve with time in several ways. We…

天体物理学 · 物理学 2010-10-27 Kazuhide Ichikawa , Tomo Takahashi

We investigate the correlation between the neutrino mass limit and dark energy with time evolving equation of state. Parameterizing dark energy as w=w_0+w_1*z/(1+z), we make a global fit using Markov Chain Monte Carlo technique to determine…

天体物理学 · 物理学 2008-11-26 Jun-Qing Xia , Gong-Bo Zhao , Xinmin Zhang

In order to test the possible interaction between dark energy and dark matter, we investigate observational constraints on a phenomenological scenario, in which the ratio between the dark energy and matter densities is proportional to the…

宇宙学与河外天体物理 · 物理学 2015-05-20 Shuo Cao , Nan Liang , Zong-Hong Zhu

We investigate a dark energy (DE) equation of state (EoS) parametrization in a curved spacetime using current observations. We constrain the model parameters by using observational Hubble data from Cosmic Chronometer (CC), Pantheon Plus…

综合物理 · 物理学 2026-05-13 D. Revanth Kumar , Santosh Kumar Yadav

Determining the equation of state of dark energy with astronomical observations is crucially important to understand the nature of dark energy. In performing a likelihood analysis of the data, especially of the cosmic microwave background…

宇宙学与河外天体物理 · 物理学 2011-07-21 Jie Liu , Mingzhe Li , Xinmin Zhang

Using the Markov chain Monte Carlo (MCMC) method we perform a global analysis constraining the dynamics of dark energy in light of the supernova (Riess "Gold" samples), galaxy clustering (SDSS 3D power spectra and SDSS lyman-\alpha forest…

天体物理学 · 物理学 2008-11-26 Gong-Bo Zhao , Jun-Qing Xia , Bo Feng , Xinmin Zhang

A simple speed-up cosmology model is proposed to account for the dark energy puzzle. We condense contributions from dark energy and curvature term into one effective parameter in order to reduce parameter degeneracies and to find any…

天体物理学 · 物理学 2007-10-10 Xin-He Meng , Meng Su , Zheng Wang

The variation of dark energy density with redshift, $\rho_X(z)$, provides a critical clue to the nature of dark energy. Since $\rho_X(z)$ depends on the dark energy equation of state $w_X(z)$ through an integral, $\rho_X(z)$ can be…

天体物理学 · 物理学 2009-11-10 Yun Wang , Katherine Freese

We apply the Constitution compilation of 397 supernova Ia, the baryon acoustic oscillation measurements including the $A$ parameter, the distance ratio and the radial data, the five-year Wilkinson microwave anisotropy probe and the Hubble…

宇宙学与河外天体物理 · 物理学 2015-03-17 Nana Pan , Yungui Gong , Yun Chen , Zong-Hong Zhu

We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of…

天体物理学 · 物理学 2008-11-26 Antony Lewis , Sarah Bridle

We use observational data from Type Ia Supernovae (SN), Baryon Acoustic Oscillations (BAO), Cosmic Microwave Background (CMB) and observational Hubble data (OHD), and the Markov Chain Monte Carlo (MCMC) method, to constrain the cosmological…

宇宙学与河外天体物理 · 物理学 2015-03-13 Jianbo Lu , Emmanuel N. Saridakis , M. R. Setare , Lixin Xu
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