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We apply the hierarchical probabilistic SED model BayeSN to analyse a sampleof 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD toinvestigate the properties of dust in their host galaxies. We jointly infer thedust law $R_V$ population distributions at the SED level in high- and low-massgalaxies simultaneously with dust-independent, intrinsic differences. We findan intrinsic mass step of $-0.049\pm0.016$ mag, at a significance of3.1$\sigma$, when allowing for a constant intrinsic, achromatic magnitudeoffset. We additionally apply a model allowing for time- andwavelength-dependent intrinsic differences between SNe Ia in different massbins, finding $\sim$2$\sigma$ differences in magnitude and colour around peakand 4.5$\sigma$ differences at later times. These intrinsic differences areinferred simultaneously with a difference in population mean $R_V$ of$\sim$2$\sigma$ significance, demonstrating that both intrinsic and extrinsicdifferences may play a role in causing the host galaxy mass step. We alsoconsider a model which allows the mean of the $R_V$ distribution to linearlyevolve with redshift but find no evidence for any evolution - we infer thegradient of this relation $\eta_R = -0.38\pm0.70$. In addition, we discuss inbrief a new, GPU-accelerated Python implementation of BayeSN suitable forapplication to large surveys which is publicly available and can be used forfuture cosmological analyses; this code can be found here:https://github.com/bayesn/bayesn.

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