A BayeSN distance ladder: <i>H</i>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 (H0) is important in cosmology, given the recent tension with the early universe inference. We estimate H0 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 H0 = 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 σint ≲ 0.1 mag. We test various selection criteria and do not find significant shifts in the estimate of H0. Simultaneous modelling of the optical and NIR yields up to ∼15% reduction in H0 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 H0 error budget.</jats:p