Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift

Abstract

We apply the hierarchical probabilistic SED model BayeSN to analyse a sample of 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law $R_V$ population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of $-0.049\pm 0.016$ mag, at a significance of 3.1$\ddot{I}ƒ$, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding $\sim$2$\ddot{I}ƒ$ differences in magnitude and colour around peak and 4.5$\ddot{I}ƒ$ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean $R_V$ of $\sim$2$\ddot{I}ƒ$ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the $R_V$ distribution to linearly evolve with redshift but find no evidence for any evolution - we infer the gradient of this relation $\hat{I}{\cdot }_R=-0.38\pm 0.70$. In addition, we discuss in brief a new, GPU-accelerated Python implementation of BayeSN suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn.

24 pages, 8 figures, 3 tables. Accepted for publication in MNRAS. BayeSN code available at https://github.com/bayesn/bayesn