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

We apply the hierarchical probabilistic spectral energy distribution (SED) model BAYESN to analyse a sample of 475 type Ia supernovae (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 ± 0.016 mag, at a significance of 3.1σ, 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 ~2σ differences in magnitude and colour around peak and 4.5σ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean R_V of ~2σ 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 η_R = -0.38 ± 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.