Abstract
We have constructed a comprehensive statistical model for Type Ia supernova (SN Ia) light curves spanning optical through near-infrared (NIR) data. A hierarchical framework coherently models multiple random and uncertain effects, including intrinsic supernova (SN) light curve covariances, dust extinction and reddening, and distances. An improved BayeSN Markov Chain Monte Carlo code computes probabilistic inferences for the hierarchical model by sampling the global probability density of parameters describing individual SNe and the population. We have applied this hierarchical model to optical and NIR data of 127 SNe Ia from PAIRITEL, CfA3, Carnegie Supernova Project, and the literature. We find an apparent population correlation between the host galaxy extinction AV and the ratio of total-to-selective dust absorption RV. For SNe with low dust extinction, AV ≲ 0.4, we find RV ≈ 2.5–2.9, while at high extinctions, AV ≳ 1, low values of RV < 2 are favored. The NIR luminosities are excellent standard candles and are less sensitive to dust extinction. They exhibit low correlation with optical peak luminosities, and thus provide independent information on distances. The combination of NIR and optical data constrains the dust extinction and improves the predictive precision of individual SN Ia distances by about 60%. Using cross-validation, we estimate an rms distance modulus prediction error of 0.11 mag for SNe with optical and NIR data versus 0.15 mag for SNe with optical data alone. Continued study of SNe Ia in the NIR is important for improving their utility as precise and accurate cosmological distance indicators.
Highlights
Type Ia supernova (SN Ia) rest-frame optical light curves have been of great utility for measuring fundamental quantities of the universe
One of the largest systematic uncertainties limiting the precision of distance estimates from rest-frame optical light curves is dust extinction in the host galaxy and the confounding of dust reddening with the intrinsic color variations of SNe Ia (Conley et al 2007)
POSTERIOR INFERENCES we report the posterior inferences of light curves and the population when the training set consists of all the SNe and their redshifts (D, Z)
Summary
Type Ia supernova (SN Ia) rest-frame optical light curves have been of great utility for measuring fundamental quantities of the universe. One of the largest systematic uncertainties limiting the precision of distance estimates from rest-frame optical light curves is dust extinction in the host galaxy and the confounding of dust reddening with the intrinsic color variations of SNe Ia (Conley et al 2007). Wood-Vasey et al (2008, hereafter WV08) compiled a sample of NIR SN Ia observations taken with the Peters Automated InfraRed Imaging TELescope (PAIRITEL; Bloom et al 2006) They found that the H-band peak absolute magnitude had small scatter σ (MH ) ≈ 0.15 mag and could provide distance estimates competitive with those derived from optical light curve shapes. We expand upon the hierarchical modeling approach for SNe Ia first described by Mandel et al (2009), and apply it to statistical modeling of SN Ia light curves in both the optical and near-infrared, including the effects of host galaxy dust. Mathematical details of the new BayeSN algorithm are given in Appendix D
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