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The effects of varying colour-luminosity relations on supernova science

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arxiv 2009.13230 v2 pith:PG6RMM2Z submitted 2020-09-28 astro-ph.CO astro-ph.GA

classification astro-ph.COastro-ph.GA
keywords betarelationcolour-luminositycolourenvironmentsfindbetterbluer
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abstract

The success of Type Ia supernova (SN Ia) distance standardisation for cosmology relies on a single global linear relationship between their peak luminosity and colour, the $\beta$ parameter. However, there are several pieces of evidence and physical reasons to believe that this relation is not universal and may change within different subgroups, or even among individual objects. In this work, we allow $\beta$ to vary among subpopulations with different observed properties in the cosmological fits. Although the inferred cosmological parameters are consistent with previous studies that assume a single colour-luminosity relation, we find that the SN data favour nonuniversal distributions of $\beta$ when split according to SN colour and/or host-galaxy mass. For galaxy mass, we obtain a $\beta$-step relation in which low $\beta$ values occur in more massive galaxies, a trend that can be explained by differing dust reddening laws for two types of environments. For colour, we find that bluer/redder SNe Ia are consistent with a lower/larger $\beta$. This trend is explained with $\beta$ being a combination of a low intrinsic colour-luminosity relation dominant in bluer SNe and a higher extrinsic reddening relation dominant at redder colours. The host galaxy mass-step correction always provides better distance calibration, regardless of the multiple $\beta$ approaches, and we suggest that it may come from a difference in intrinsic colour-luminosity properties of SNe Ia in two types of environments. Additionally, we find that blue SNe in low-mass environments are better standard candles than the others.

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    A simulation-based inference pipeline (Stjörnumál) fits SN Ia dust and intrinsic scatter models to DES 5-year data, enabling fast Bayesian model comparison across seven SN Ia population models.

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