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Register a new userKinematic Constraints on Spatial Curvature from Supernovae Ia and Cosmic Chronometers
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Added by
Jos\\'e Fernando de Jesus
Publication date
2019
fields
Physics
and research's language is
English
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An approach to estimate the spatial curvature $Omega_k$ from data independently of dynamical models is suggested, through kinematic parameterizations of the comoving distance ($D_{C}(z)$) with third degree polynomial, of the Hubble parameter ($H(z)$) with a second degree polynomial and of the deceleration parameter ($q(z)$) with first order polynomial. All these parameterizations were done as function of redshift $z$. We used SNe Ia dataset from Pantheon compilation with 1048 distance moduli estimated in the range $0.01<z<2.3$ with systematic and statistical errors and a compilation of 31 $H(z)$ data estimated from cosmic chronometers. The spatial curvature found for $D_C(z)$ parametrization was $Omega_{k}=-0.03^{+0.24+0.56}_{-0.30-0.53}$. The parametrization for deceleration parameter $q(z)$ resulted in $Omega_{k}=-0.08^{+0.21+0.54}_{-0.27-0.45}$. The $H(z)$ parametrization has shown incompatibilities between $H(z)$ and SNe Ia data constraints, so these analyses were not combined. The $D_C(z)$ and $q(z)$ parametrizations are compatible with the spatially flat Universe as predicted by many inflation models and data from CMB. This type of analysis is very appealing as it avoids any bias because it does not depend on assumptions about the matter content of the Universe for estimating $Omega_k$.
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Applying the distance sum rule in strong gravitational lensing (SGL) and type Ia supernova (SN Ia) observations, one can provide an interesting cosmological model-independent method to determine the cosmic curvature parameter $Omega_k$. In this paper, with the newly compiled data sets including 161 galactic-scale SGL systems and 1048 SN Ia data, we place constraints on $Omega_k$ within the framework of three types of lens models extensively used in SGL studies. Moreover, to investigate the effect of different mass lens samples on the results, we divide the SGL sample into three sub-samples based on the center velocity dispersion of intervening galaxies. In the singular isothermal sphere (SIS) and extended power-law lens models, a flat universe is supported with the uncertainty about 0.2, while a closed universe is preferred in the power-law lens model. We find that the choice of lens models and the classification of SGL data actually can influence the constraints on $Omega_k$ significantly.
We carry out a test of the cosmic distance duality relation using a sample of 52 SPT-SZ clusters, along with X-ray measurements from XMM-Newton. To carry out this test, we need an estimate of the luminosity distance ($D_L$) at the redshift of the cluster. For this purpose, we use three independent methods: directly using $D_L$ from the closest Type Ia Supernovae from the Union 2.1 sample, non-parametric reconstruction of $D_L$ using the same Union 2.1 sample, and finally using $H(z)$ measurements from cosmic chronometers and reconstructing $D_L$ using Gaussian Process regression. We use four different functions to characterize the deviations from CDDR. All our results for these ($4 times 3$) analyses are consistent with CDDR to within 1$sigma$.
We study observational constraints on the cosmographic functions up to the fourth derivative of the scale factor with respect to cosmic time, i.e., the so-called snap function, using the non-parametric method of Gaussian Processes. As observational data we use the Hubble parameter data. Also we use mock data sets to estimate the future forecast and study the performance of this type of data to constrain cosmographic functions. The combination between a non-parametric method and the Hubble parameter data is investigated as a strategy to reconstruct cosmographic functions. In addition, our results are quite general because they are not restricted to a specific type of functional dependency of the Hubble parameter. We investigate some advantages of using cosmographic functions instead of cosmographic series, since the former are general definitions free of approximations. In general, our results do not deviate significantly from $Lambda CDM$. We determine a transition redshift $z_{tr}=0.637^{+0.165}_{-0.175}$ and $H_{0}=69.45 pm 4.34$. Also assuming priors for the Hubble constant we obtain $z_{tr}=0.670^{+0.210}_{-0.120}$ with $H_{0}=67.44$ (Planck) and $z_{tr}=0.710^{+0.159}_{-0.111}$ with $H_{0}=74.03$(SH0ES). Our main results are summarized in table 2.
In this work we perform an observational data analysis on Einsteinian cubic gravity and $f(P)$ gravity with the objective of constraining the parameter space of the theories. We use the 30 point $z-H(z)$ cosmic chronometer data as the observational tool for our analysis along with the BAO and the CMB peak parameters. The $chi^2$ statistic is used for the fitting analysis and it is minimized to obtain the best fit values for the free model parameters. We have used the Markov chain Monte Carlo algorithm to obtain bounds for the free parameters. To achieve this we used the publicly available textit{CosmoMC} code to put parameter bounds and subsequently generate contour plots for them with different confidence intervals. Besides finding the Hubble parameter $H$ in terms of the redshift $z$ theoretically from our gravity models, we have exercised correlation coefficients and two textit{machine learning} models, namely the linear regression (LR) and artificial neural network (ANN), for the estimation of $H(z)$. For this purpose, we have developed a textit{Python} package for finding the parameter space, performing the subsequent statistical analysis and prediction analysis using machine learning. We compared both of our theoretical and estimated values of $H(z)$ with the observations. It is seen that our theoretical and estimated models from machine learning performed significantly well when compared with the observations.
The standard model of cosmology is founded on the basis that the expansion rate of the universe is accelerating at present --- as was inferred originally from the Hubble diagram of Type Ia supernovae. There exists now a much bigger database of supernovae so we can perform rigorous statistical tests to check whether these standardisable candles indeed indicate cosmic acceleration. Taking account of the empirical procedure by which corrections are made to their absolute magnitudes to allow for the varying shape of the light curve and extinction by dust, we find, rather surprisingly, that the data are still quite consistent with a constant rate of expansion.
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