A BayeSN Distance Ladder: $H_0$ from a consistent modelling of Type Ia supernovae from the optical to the near infrared

Abstract

The local distance ladder estimate of the Hubble constant ($H_0$) is important in cosmology, given the recent tension with the early universe inference. We estimate $H_0$ from the Type Ia supernova (SN~Ia) distance ladder, inferring SN~Ia distances with the hierarchical Bayesian SED model, BayeSN. This method has a notable advantage of being able to continuously model the optical and near-infrared (NIR) SN~Ia light curves simultaneously. We use two independent distance indicators, Cepheids or the tip of the red giant branch (TRGB), to calibrate a Hubble-flow sample of 67 SNe~Ia with optical and NIR data. We estimate $H_0=74.82\pm 0.97$ (stat) $\pm 0.84$ (sys) km\,s${}^{-1}$\,Mpc${}^{-1}$ when using the calibration with Cepheid distances to 37 host galaxies of 41 SNe~Ia, and $70.92\pm 1.14$ (stat) $\pm 1.49$ (sys) km\,s${}^{-1}$\,Mpc${}^{-1}$ when using the calibration with TRGB distances to 15 host galaxies of 18 SNe~Ia. For both methods, we find a low intrinsic scatter ${\sigma }_{\mathrm{int}}\lesssim 0.1$ mag. We test various selection criteria and do not find significant shifts in the estimate of $H_0$. Simultaneous modelling of the optical and NIR yields up to $\sim$15\% reduction in $H_0$ uncertainty compared to the equivalent optical-only cases. With improvements expected in other rungs of the distance ladder, leveraging joint optical-NIR SN~Ia data can be critical to reducing the $H_0$ error budget.

10 pages, 8 figures, published in MNRAS

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