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Register a new userTest of the cosmic evolution using Gaussian processes
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Much focus was on the possible slowing down of cosmic acceleration under the dark energy parametrization. In the present paper, we investigate this subject using the Gaussian processes (GP), without resorting to a particular template of dark energy. The reconstruction is carried out by abundant data including luminosity distance from Union2, Union2.1 compilation and gamma-ray burst, and dynamical Hubble parameter. It suggests that slowing down of cosmic acceleration cannot be presented within 95% C.L., in considering the influence of spatial curvature and Hubble constant. In order to reveal the reason of tension between our reconstruction and previous parametrization constraint for Union2 data, we compare them and find that slowing down of acceleration in some parametrization is only a mirage. Although these parameterizations fits well with the observational data, their tension can be revealed by high order derivative of distance $D$. Instead, GP method is able to faithfully model the cosmic expansion history.
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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 letter, we implement a test of the standard law for the dark matter density evolution. For this purpose, only a flat universe and the validity of the FRW metric are assumed. A deformed dark matter density evolution law is considered, given by $rho_c(z) propto (1+z)^{3+epsilon}$, and constraints on $epsilon$ are obtained by using galaxy cluster gas mass fractions, and cosmic chronometers measurements. We find that $epsilon =0$ within 2$sigma$ c.l., in full agreement with other recent analyses.
We study the performance of the hybrid template-machine-learning photometric redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We calibrate the fluxes of the $40$ PAUS narrow bands with $6$ broadband fluxes ($uBVriz$) in the COSMOS field using three different methods, including a new method which utilises the correlation between the apparent size and overall flux of the galaxy. We use a rich set of empirically derived galaxy spectral templates as guides to train the Gaussian process, and we show that our results are competitive with other standard photometric redshift algorithms. Delight achieves a photo-$z$ $68$th percentile error of $sigma_{68}=0.0081(1+z)$ without any quality cut for galaxies with $i_mathrm{auto}<22.5$ as compared to $0.0089(1+z)$ and $0.0202(1+z)$ for the BPz and ANNz2 codes, respectively. Delight is also shown to produce more accurate probability distribution functions for individual redshift estimates than BPz and ANNz2. Common photo-$z$ outliers of Delight and BCNz2 (previously applied to PAUS) are found to be primarily caused by outliers in the narrowband fluxes, with a small number of cases potentially indicating spectroscopic redshift failures in the reference sample. In the process, we introduce performance metrics derived from the results of BCNz2 and Delight, allowing us to achieve a photo-$z$ quality of $sigma_{68}<0.0035(1+z)$ at a magnitude of $i_mathrm{auto}<22.5$ while keeping $50$ per cent objects of the galaxy sample.
Inflation predicts that the Universe is spatially flat. The Planck 2018 measurements of the cosmic microwave background anisotropy favour a spatially closed universe at more than 2$sigma$ confidence level. We use model independent methods to study the issue of cosmic curvature. The method reconstructs the Hubble parameter $H(z)$ from cosmic chronometers data with the Gaussian process method. The distance modulus is then calculated with the reconstructed function $H(z)$ and fitted by type Ia supernovae data. Combining the cosmic chronometers and type Ia supernovae data, we obtain $Omega_{k0}h^2=0.102pm 0.066$ which is consistent with a spatially flat universe at the 2$sigma$ confidence level. By adding the redshift space distortions data to the type Ia supernovae data with a proposed novel model independent method, we obtain $Omega_{k0}h^2=0.117^{+0.058}_{-0.045}$ and no deviation from $Lambda$CDM model is found.
We present methods for emulating the matter power spectrum which effectively combine information from cosmological $N$-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a handful of simulations. We present the first implementation of multi-fidelity emulation in cosmology, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrums dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multi-fidelity emulator can achieve percent-level accuracy on average with only $3$ high-fidelity simulations and outperforms a single-fidelity emulator that uses $11$ simulations. With a fixed number of high-fidelity training simulations, we show that our multi-fidelity emulator is $simeq 100$ times better than a single-fidelity emulator at $k leq 2 ,htextrm{Mpc}{^{-1}}$, and $simeq 20$ times better at $3 leq k < 6.4 ,htextrm{Mpc}{^{-1}}$. Multi-fidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities.
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