While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in near-infrared (NIR) light, which is significantly less sensitive to dust extinction. A SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia datasets from ground- and space-based telescopes including HST, LSST, JWST, and RST. We construct a hierarchical Bayesian model for SN Ia SEDs, continuous over time and wavelength, from the optical to NIR (B through H, or 0.35 − 1.8 μm). We model the SED as a combination of physically-distinct host galaxy dust and intrinsic spectral components. The distribution of intrinsic SEDs over time and wavelength is modelled with probabilistic functional principal components and the covariance of residual functions. We train the model on a nearby sample of 79 SNe Ia with joint optical and NIR light curves by sampling the global posterior distribution over dust and intrinsic latent variables, SED components and population hyperparameters. Photometric distances of SNe Ia with NIR data near maximum obtain a total RMS error of 0.10 mag with our BayeSN model, compared to 0.13-0.14 mag with SALT2 and SNooPy for the same sample. Jointly fitting the optical and NIR data of the full sample up to moderate reddening (host E(B − V) < 0.4) for a global host dust law, we find RV = 2.9 ± 0.2, consistent with the Milky Way average.

We apply BayeSN, our new hierarchical Bayesian model for the SEDs of Type Ia supernovae (SNe Ia), to analyse the griz light curves of 157 nearby SNe Ia (0.015 < z < 0.08) from the public Foundation DR1 dataset. We train a new version of BayeSN, continuous from 0.35–0.95 μm, which we use to model the properties of SNe Ia in the rest-frame z-band, study the properties of dust in their host galaxies, and construct a Hubble diagram of SN Ia distances determined from full griz light curves. Our griz Hubble diagram has a low total RMS of 0.13 mag using BayeSN, compared to 0.16 mag using SALT2. Additionally, we test the consistency of the dust law RV between low- and high-mass host galaxies by using our model to fit the full time- and wavelength-dependent SEDs of SNe Ia up to moderate reddening (peak apparent B − V ≲ 0.3). Splitting the population at the median host mass, we find RV = 2.84 ± 0.31 in low-mass hosts, and RV = 2.58 ± 0.23 in high-mass hosts, both consistent with the global value of RV = 2.61 ± 0.21 that we estimate for the full sample. For all choices of mass split we consider, RV is consistent across the step within ≲ 1.2σ. Modelling population distributions of dust laws in low- and high-mass hosts, we find that both subsamples are highly consistent with the full sample’s population mean μ(RV) = 2.70 ± 0.25 with a 95 per cent upper bound on the population σ(RV) < 0.61. The RV population means are consistent within ≲ 1.2σ. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step.

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Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲ z ≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-z HST data with 42 SNe Ia at z < 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter, w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σ significance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 + w = −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measure H 0 = 75.9 ± 2.2 km s−1 Mpc−1 from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versus H 0 = 71.2 ± 3.8 km s−1 Mpc−1 using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 + w = −0.10 ± 0.09, and with optical and NIR data combined, we find 1 + w = −0.06 ± 0.07; these shifts of up to ∼0.11 in w could point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-z samples, new light-curve models, calibration improvements, and eventually by building high-z samples from the Roman Space Telescope.

In late 2014, four images of supernova (SN) “Refsdal,” the first known example of a strongly lensed SN with multiple resolved images, were detected in the MACS J1149 galaxy-cluster field. Following the images’ discovery, the SN was predicted to reappear within hundreds of days at a new position ∼8″ away in the field. The observed reappearance in late 2015 makes it possible to carry out Refsdal’s original proposal to use a multiply imaged SN to measure the Hubble constant H 0, since the time delay between appearances should vary inversely with H 0. Moreover, the position, brightness, and timing of the reappearance enable a novel test of the blind predictions of galaxy-cluster models, which are typically constrained only by the positions of multiply imaged galaxies. We have developed a new photometry pipeline that uses DOLPHOT to measure the fluxes of the five images of SN Refsdal from difference images. We apply four separate techniques to perform a blind measurement of the relative time delays and magnification ratios between the last image SX and the earlier images S1–S4. We measure the relative time delay of SX–S1 to be 376.0 − 5.5 + 5.6 days and the relative magnification to be 0.30 − 0.3 + 0.5 . This corresponds to a 1.5% precision on the time delay and 17% precision for the magnification ratios and includes uncertainties due to millilensing and microlensing. In an accompanying paper, we place initial and blind constraints on the value of the Hubble constant.