학술논문
Training custom light curve models of SN Ia subpopulations selected according to host galaxy properties.
Document Type
Article
Author
Source
Subject
*LIGHT curves
*TYPE I supernovae
*SPECTRAL energy distribution
*DARK energy
*GALAXIES
*STELLAR luminosity function
*SUPERNOVAE
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Language
ISSN
0035-8711
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 (|$M \gtrsim 10^{10} \, {\rm M}_{\odot }$|) versus low-mass galaxies]. We present a novel method for including host-mass correlations in the SALT3 (Spectral Adaptive Light curve Template 3) light curve model used for standardizing 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 spectral energy distribution of SNe from low-mass galaxies is bluer than the high-mass counterpart. We then use our trained models to perform an SN cosmology analysis on the 3-yr 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. [ABSTRACT FROM AUTHOR]