Abstract
ABSTRACT 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. An SN Ia spectral energy distribution (SED) model capable of fitting rest-frame NIR observations is necessary to fully leverage current and future SN Ia data sets 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 $0.35{-}1.8\, \mu$m). 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.
Highlights
Type Ia supernovae (SNe Ia) are effective cosmological probes as “standardiseable candles”: their peak luminosities can be inferred from their optical light curve shapes and colours, so their distances can be estimated from their apparent brightnesses
Beyond the datasets analysed in the present work, the ability to effectively leverage joint optical and NIR observations is crucial for fully exploiting a number of recent and current surveys and forthcoming datasets, including the Carnegie Supernova ProjectII (CSP-II; Phillips et al 2019), the Foundation Supernova Survey (Foley et al 2018b) and Young Supernova Experiment with Pan-STARRS, RAISIN (GO-13046, GO-14216) and Supernovae in the Infrared avec Hubble (SIRAH) (GO-15889) with the Hubble Space Telescope (HST), the ESO VISTA Extragalactic Infrared Legacy Survey (VEILS), and the DEHVILS Survey using UKIRT
As with SALT2, we report the statistical uncertainties on the fit parameters derived from inverting the Hessian matrix at the best-fit parameters, and we have adjusted the SNooPy distance estimates to a scale of H0 = 73.24 km s−1 Mpc−1
Summary
The current global sample used for cosmology, derived from the SDSS-II, SNLS, Pan-STARRS (PS1), low-z and HST surveys, has grown to over a thousand SNe Ia (Pantheon; Scolnic et al 2018). Beyond the datasets analysed in the present work, the ability to effectively leverage joint optical and NIR observations is crucial for fully exploiting a number of recent and current surveys and forthcoming datasets, including the Carnegie Supernova ProjectII (CSP-II; Phillips et al 2019), the Foundation Supernova Survey (Foley et al 2018b) and Young Supernova Experiment with Pan-STARRS, RAISIN (GO-13046, GO-14216) and SIRAH (GO-15889) with the Hubble Space Telescope (HST), the ESO VISTA Extragalactic Infrared Legacy Survey (VEILS), and the DEHVILS Survey using UKIRT This is important for LSST, which will observe SNe Ia in ugrizy, and will probe rest-frame z or y to redshifts z 0.3. The Nancy Grace Roman Space Telescope (RST, formerly WFIRST) will have a dedicated SN survey and its wide NIR filters will probe rest-frame Y JH out to redshifts z 1, 0.7, 0.4 respectively
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