H0 from cosmic chronometers and type Ia supernovae, with Gaussian processes and the novel weighted polynomial regression method

• Verfasst von: Gómez-Valent, Adrià [VerfasserIn]
• Verfasst von: Amendola, Luca [VerfasserIn]
• Titel: H0 from cosmic chronometers and type Ia supernovae, with Gaussian processes and the novel weighted polynomial regression method
• Verf.angabe: Adrià Gómez-Valent and Luca Amendola
• Jahr: 2018
• Umfang: 42 S.
• Fussnoten: Im Titel ist die Zahl "0" nach H tiefgestellt ; Gesehen am 24.11.2020
• Titel Quelle: Enthalten in: De.arxiv.org
• Ort Quelle: [S.l.] : Arxiv.org, 1991
• Jahr Quelle: 2018
• Band/Heft Quelle: (2018) Artikel-Nummer 1802.01505, 42 Seiten
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Astrophysics - Cosmology and Nongalactic Astrophysics
• Sach-SW: General Relativity and Quantum Cosmology
• K10plus-PPN: 1570593787

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 (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.