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

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 \(-0.049\pm0.016\) mag, at a significance of 3.1\(\sigma\), 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\(\sigma\) differences in magnitude and colour around peak and 4.5\(\sigma\) differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean \(R_V\) of \(\sim\)2\(\sigma\) 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 \(\eta_R = -0.38\pm0.70\). 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.