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arXiv · 1802.01505

$H_0$ from cosmic chronometers and Type Ia supernovae, with Gaussian Processes and the novel Weighted Polynomial Regression method

Abstract

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 (2018). 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 $2.71σ$ 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. In addition, we present a novel method to reconstruct functions from data, which consists in a weighted sum of polynomial regressions (WPR). We apply it from a cosmographic perspective to reconstruct $H(z)$ and estimate $H_0$ from CCH and SnIa measurements. The result obtained with this method, $H_0=(68.90\pm 1.96)$ km/s/Mpc, is fully compatible with the GPs ones. Finally, a more conservative GPs+WPR value is also provided, $H_0=(68.45\pm 2.00)$ km/s/Mpc, which is still almost $2σ$ away from $H_0^{\rm HST}$.

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BibTeXRIS

Adrià Gómez-Valent, Luca Amendola. 2018-04-09. $H_0$ from cosmic chronometers and Type Ia supernovae, with Gaussian Processes and the novel Weighted Polynomial Regression method. https://doi.org/10.1088/1475-7516%2F2018%2F04%2F051

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