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Register a new userTesting cosmic opacity from SNe Ia and Hubble parameter through three cosmological-model-independent methods
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We use the newly published 28 observational Hubble parameter data ($H(z)$) and current largest SNe Ia samples (Union2.1) to test whether the universe is transparent. Three cosmological-model-independent methods (nearby SNe Ia method, interpolation method and smoothing method) are proposed through comparing opacity-free distance modulus from Hubble parameter data and opacity-dependent distance modulus from SNe Ia . Two parameterizations, $tau(z)=2epsilon z$ and $tau(z)=(1+z)^{2epsilon}-1$ are adopted for the optical depth associated to the cosmic absorption. We find that the results are not sensitive to the methods and parameterizations. Our results support a transparent universe.
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We perform a model independent reconstruction of the cosmic expansion rate based on type Ia supernova data. Using the Union 2.1 data set, we show that the Hubble parameter behaviour allowed by the data without making any hypothesis about cosmological model or underlying gravity theory is consistent with a flat LCDM universe having H_0 = 70.43 +- 0.33 and Omega_m=0.297 +- 0.020, weakly dependent on the choice of initial scatter matrix. This is in closer agreement with the recently released Planck results (H_0 = 67.3 +- 1.2, Omega_m = 0.314 +- 0.020) than other standard analyses based on type Ia supernova data. We argue this might be an indication that, in order to tackle subtle deviations from the standard cosmological model present in type Ia supernova data, it is mandatory to go beyond parametrized approaches.
The nearby, bright, almost completely unreddened Type Ia supernova 2011fe in M101 provides a unique opportunity to test both the precision and the accuracy of the extragalactic distances derived from SNe Ia light curve fitters. We apply the current, publ
We use current measurements of the expansion rate $H(z)$ and cosmic background radiation bounds on the spatial curvature of the Universe to impose cosmological model-independent constraints on cosmic opacity. To perform our analyses, we compare opacity-free distance modulus from $H(z)$ data with those from two supernovae Ia compilations: the Union2.1 plus the most distant spectroscopically confirmed SNe Ia (SNe Ia SCP-0401 $z=1.713$) and two Sloan Digital Sky Survey (SDSS) subsamples. The influence of different SNe Ia light-curve fitters (SALT2 and MLCS2K2) on the results is also verified. We find that a completely transparent universe is in agreement with the largest sample in our analysis (Union 2.1 plus SNe Ia SCP-0401). For SDSS sample a such universe it is compatible at $< 1.5sigma$ level regardless the SNe Ia light-curve fitting used.
In this paper, we present a scheme to investigate the opacity of the Universe in a cosmological-model-independent way, with the combination of current and future measurements of type Ia supernova sample and galactic-scale strong gravitational lensing systems with SNe Ia acting as background sources. The observational data include the current newly-compiled SNe Ia data (Pantheon sample) and simulated sample of SNe Ia observed by the forthcoming Large Synoptic Survey Telescope (LSST) survey, which are taken for luminosity distances ($D_L$) possibly affected by the cosmic opacity, as well as strongly lensed SNe Ia observed by the LSST, which are responsible for providing the observed time-delay distance ($D_{Delta t}$) unaffected by the cosmic opacity. Two parameterizations, $tau(z)=2beta z$ and $tau(z)=(1+z)^{2beta}-1$ are adopted for the optical depth associated to the cosmic absorption. Focusing on only one specific type of standard cosmological probe, this provides an original method to measure cosmic opacity at high precision. Working on the simulated sample of strongly lensed SNe Ia observed by the LSST in 10 year $z$-band search, our results show that, with the combination of the current newly-compiled SNe Ia data (Pantheon sample), there is no significant deviation from the transparency of the Universe at the current observational data level. Moreover, strongly lensed SNe Ia in a 10 year LSST $z$-band search would produce more robust constraints on the validity of cosmic transparency (at the precision of $Deltabeta=10^{-2}$), with a larger sample of unlensed SNe Ia detected in future LSST survey. We have also discussed the ways in which our methodology could be improved, with the combination of current and future available data in gravitational wave (GW) and electromagnetic (EM) domain.
The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $Lambda$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,Omega_m,Omega_Lambda$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.
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