Figure 1 - available via license: Creative Commons Attribution 4.0 International
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Schematic overview of our analysis pipeline, from simulation of the underlying SED model to cosmological parameter constraints. The arrows show the flow of the pipeline and how each stage is connected.
Source publication
Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low- z o...
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Context 1
... change was applied only to the legacy low-z simulations because we found that simulations based on the Scolnic & Kessler (2016) x 1 /c populations did not fully match the K21 data ( Figure B1). We note that this is not a deficiency of the Scolnic & Kessler (2016) results; rather, the K21 training data includes additional SNe that were not considered in Scolnic & Kessler (2016). ...
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... legacy low-z data, while a limited subset of the training, contain a majority of the spectra used in the training and also constitute some of the highest-S/N and best-sampled photometry. Figure B1 shows the color distributions of the low-z training data from our original baseline samples, the regenerated K21-like baseline samples, and the K21 data. We find that the K21-like simulations (and the K21 data) have significantly more SNe with blue colors when compared to the original simulations, which is likely to be due to the addition of new data from the CfA4 survey, the CSP survey, and z < 0.01 SNe that are too nearby to be suitable for dark energy measurements (but can be used for light-curve training). ...
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Citations
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 ≳ 1010 M⊙) 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 (x0, x1, 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.
