A fully photometric approach to type Ia supernova cosmology in the LSST era: host-galaxy redshifts and supernova classification

Abstract

The upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) is expected to discover nearly one million Type Ia supernovae (SNe Ia), offering an unprecedented opportunity to constrain dark energy. The vast majority of these events will lack spectroscopic classification and redshifts, necessitating a fully photometric approach to maximize cosmology constraining power. We present detailed simulations based on the Extended LSST Astronomical Time Series Classification Challenge, and a cosmological analysis using photometrically classified SNe Ia with host-galaxy photometric redshifts. This dataset features realistic multiband light curves, non–SN Ia contamination, host mis-associations, and transient–host correlations across the high-redshift Deep Drilling Fields (∼50 deg2). We also include a spectroscopically confirmed low-redshift sample based on the Wide Fast Deep fields. We employ a joint SN + host photometric redshift fit, a neural-network-based photometric classifier (Supernova Classification with a Convolutional Neural Network), and the Bayesian Estimation Applied to Multiple Species with Bias Corrections methodology to construct a bias-corrected Hubble diagram. We produce statistical + systematic covariance matrices, and perform cosmology fitting with a prior using cosmic microwave background constraints. We fit and present results for the wCDM dark energy model and the more general Chevallier–Polarski–Linder w0waCDM model. With a simulated sample of ∼6000 events, we achieve a figure of merit (FoM) value of about 150, which is significantly larger than the DES-SN5YR FoM of 54. Averaging the analysis results over 25 independent samples, we find small but significant biases indicating a need for further analysis testing and development.