学术文献库

A large fraction of Type Ia supernova (SN Ia) observations over the next decade will be in the near-infrared (NIR), at wavelengths beyond the reach of the current standard light-curve model for SN Ia cosmology, SALT3 ($\sim 2800$--8700$A$ central filter wavelength). To harness this new SN Ia sample and reduce future light-curve standardization systematic uncertainties, we train SALT3 at NIR wavelengths (SALT3-NIR) up to 2 $μ$m with the open-source model-training software SALTShaker, which can easily accommodate future observations. Using simulated data we show that the training process constrains the NIR model to $\sim 2$--3% across the phase range ($-20$ to $50$ days). We find that Hubble residual (HR) scatter is smaller using the NIR alone or optical+NIR compared to optical alone, by up to $\sim 30$% depending on filter choice (95% confidence). There is significant correlation between NIR light-curve stretch measurements and luminosity, with stretch and color corrections often improving HR scatter by up to $\sim20%$. For SN Ia observations expected from the \textit{Roman Space Telescope}, SALT3-NIR increases the amount of usable data in the SALT framework by $\sim 20$% at redshift $z\lesssim0.4$ and by $\sim 50$% at $z\lesssim0.15$. The SALT3-NIR model is part of the open-source {\tt SNCosmo} and {\tt SNANA} SN Ia cosmology packages.