A BayeSN distance ladder: H0 from a consistent modelling of Type Ia supernovae from the optical to the near-infrared

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 +/- 0.97 (stat) +/- 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 +/- 1.14 (stat) +/- 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(int) less than or similar to 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 similar to 15 per cent 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.