Abstract
Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new
approaches to classifying SNe and obtaining their redshifts without spectra if
they wish to reach their full potential. We present here a novel approach that
uses only photometry to identify SNe Ia in the 5-year Dark Energy Survey (DES)
dataset using the SUPERNNOVA classifier. Our approach, which does not rely on
any information from the SN host-galaxy, recovers SNe Ia that might otherwise
be lost due to a lack of an identifiable host. We select 2,298 high-quality SNe
Ia from the DES 5-year dataset. More than 700 of these have no spectroscopic
host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology
analysis. To analyse these SNe Ia, we derive their redshifts and properties
using only their light-curves with a modified version of the SALT2 light-curve
fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our
new sample has in average higher redshift, bluer and broader light-curves, and
fainter host-galaxies. Future surveys such as LSST will also face an additional
challenge, the scarcity of spectroscopic resources for follow-up. When applying
our novel method to DES data, we reduce the need for follow-up by a factor of
four and three for host-galaxy and live SN respectively compared to earlier
approaches. Our novel method thus leads to better optimisation of spectroscopic
resources for follow-up.