H0 from cosmic chronometers and type Ia supernovae, with Gaussian processes and the novel weighted polynomial regression method
In this paper we present new constraints on the Hubble parameter $H_0$ using: (i) the available data on $H(z)$ obtained from cosmic chronometers (CCH); (ii) the Hubble rate data points extracted from the supernovae of Type Ia (SnIa) of the Pantheon compilation and the Hubble Space Telescope (HST) CA...
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| Main Authors: | , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
2018
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| In: |
Arxiv
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| Online Access: | Verlag, kostenfrei, Volltext: http://arxiv.org/abs/1802.01505
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| Author Notes: | Adrià Gómez-Valent and Luca Amendola |
| Summary: | In this paper we present new constraints on the Hubble parameter $H_0$ using: (i) the available data on $H(z)$ obtained from cosmic chronometers (CCH); (ii) the Hubble rate data points extracted from the supernovae of Type Ia (SnIa) of the Pantheon compilation and the Hubble Space Telescope (HST) CANDELS and CLASH Multy-Cycle Treasury (MCT) programs; and (iii) the local HST measurement of $H_0$ provided by Riess et al. 2018, $H_0^{{\rm HST}}=(73.45\pm1.66)$ km/s/Mpc. Various determinations of $H_0$ using the Gaussian processes (GPs) method and the most updated list of CCH data have been recently provided by Yu, Ratra and Wang (2017). Using the Gaussian kernel they find $H_0=(67.42\pm 4.75)$ km/s/Mpc. Here we extend their analysis to also include the most released and complete set of SnIa data, which allows us to reduce the uncertainty by a factor $\sim 3$ with respect to the result found by only considering the CCH information. We obtain $H_0=(67.06\pm 1.68)$ km/s/Mpc, which favors again the lower range of values for $H_0$ and is in tension with $H_0^{{\rm HST}}$. The tension reaches the $2.71\sigma$ level. We round off the GPs determination too by taking also into account the error propagation of the kernel hyperparameters when the CCH with and without $H_0^{{\rm HST}}$ are used in the analysis. Finally, we present a novel method to reconstruct functions from data, which consists in a weighted sum of polynomial regressions. We apply it from a cosmographic perspective to reconstruct $H(z)$ and estimate $H_0$ from CCH and the $H_0^{{\rm HST}}$ measurement with two alternative approaches. The results obtained with this method are fully compatible with the ones obtained with GPs. |
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| Physical Description: | Online Resource |