Constraining the SN Ia Host Galaxy Dust Law Distribution and Mass Step: Hierarchical BayeSN Analysis of Optical and Near-Infrared Light Curves
| Autor: | Thorp, Stephen, Mandel, Kaisey S. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | MNRAS 517, 2360-2382 (2022) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1093/mnras/stac2714 |
| Popis: | We use the BayeSN hierarchical probabilistic SED model to analyse the optical-NIR ($BVriYJH$) light curves of 86 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project to investigate the SN Ia host galaxy dust law distribution and correlations between SN Ia Hubble residuals and host mass. Our Bayesian analysis simultaneously constrains the mass step and dust $R_V$ population distribution by leveraging optical-NIR colour information. We demonstrate how a simplistic analysis where individual $R_V$ values are first estimated for each SN separately, and then the sample variance of these point estimates is computed, overestimates the $R_V$ population variance $\sigma_R^2$. This bias is exacerbated when neglecting residual intrinsic colour variation beyond that due to light curve shape. Instead, Bayesian shrinkage estimates of $\sigma_R$ are more accurate, with fully hierarchical analysis of the light curves being ideal. For the 75 SNe with low-to-moderate reddening (peak apparent $B-V\leq0.3$), we estimate an $R_V$ distribution with population mean $\mu_R=2.59\pm0.14$, and standard deviation $\sigma_R=0.62\pm0.16$. Splitting this subsample at the median host galaxy mass ($10^{10.57}~\mathrm{M}_\odot$) yields consistent estimated $R_V$ distributions between low- and high-mass galaxies, with $\mu_R=2.79\pm0.18$, $\sigma_R=0.42\pm0.24$, and $\mu_R=2.35\pm0.27$, $\sigma_R=0.74\pm0.36$, respectively. When estimating distances from the full optical-NIR light curves while marginalising over various forms of the dust $R_V$ distribution, a mass step of $\gtrsim0.06$ mag persists in the Hubble residuals at the median host mass. Comment: 24 pages, 10 figures. Accepted for publication in MNRAS |
| Databáze: | arXiv |
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