Training Custom Light Curve Models of SN Ia Sub-Populations Selected According to Host Galaxy Properties

Abstract: Type Ia supernova (SN Ia) cosmology analyses include a luminosity step function in their distance standardization process to account for an observed yet unexplained difference in the post-standardization luminosities of SNe Ia originating from different host galaxy populations (e.g., high-mass ($M \gtrsim 10^{10} M_{\odot}$) versus low-mass galaxies). We present a novel method for including host-mass correlations in the SALT3 light curve model used for standardising SN Ia distances. We split the SALT3 training sample according to host-mass, training independent models for the low- and high-host-mass samples. Our models indicate that there are different average Si II spectral feature strengths between the two populations, and that the average SED of SNe from low-mass galaxies is bluer than the high-mass counterpart. We then use our trained models to perform a SN cosmology analysis on the 3-year spectroscopically confirmed Dark Energy Survey SN sample, treating SNe from low- and high-mass host galaxies as separate populations throughout. We find that our mass-split models reduce the Hubble residual scatter in the sample, albeit at a low statistical significance. We do find a reduction in the mass-correlated luminosity step but conclude that this arises from the model-dependent re-definition of the fiducial SN absolute magnitude rather than the models themselves. Our results stress the importance of adopting a standard definition of the SN parameters ($x_0, x_1, c$) in order to extract the most value out of the light curve modelling tools that are currently available and to correctly interpret results that are fit with different models.