The snapshot distance method: estimating the distance to a Type Ia supernova from minimal observations

Abstract

We present the snapshot distance method (SDM), a modern incarnation of a proposed technique for estimating the distance to a Type Ia supernova (SN Ia) from minimal observations. Our method, which has become possible owing to recent work in the application of deep learning to SN Ia spectra (we use the deepSIP package), allows us to estimate the distance to an SN Ia from a single optical spectrum and epoch of 2+ passband photometry -one night's worth of observations (though contemporaneity is not a requirement). Using a compilation of well-observed SNe Ia, we generate snapshot distances across a wide range of spectral and photometric phases, light-curve shapes, photometric passband combinations, and spectrum signal-to-noise ratios. By comparing these estimates to the corresponding distances derived from fitting all available photometry for each object, we demonstrate that our method is robust to the relative temporal sampling of the provided spectroscopic and photometric information, and to a broad range of light-curve shapes that lie within the domain of standard width-luminosity relations. Indeed, the median residual (and asymmetric scatter) between SDM distances derived from two-passband photometry and conventional light-curve-derived distances that utilise all available photometry is 0.013 +0.154 −0.143 mag. Moreover, we find that the time of maximum brightness and light-curve shape (both of which are spectroscopically derived in our method) are only minimally responsible for the observed scatter. In a companion paper, we apply the SDM to a large number of sparsely observed SNe Ia as part of a cosmological study.

Peculiar-velocity cosmology with Types Ia and II supernovae

“…With the advent of deepSIP (Stahl et al 2020b), this requirement and the waste it incurs can be mitigated if an optical spectrum is available -by using a sophisticated convolutional neural network trained on a significant fraction of all relevant SN Ia observations, deepSIP is able to map the spectrum of an SN Ia to its corresponding light-curve shape with impressive precision. In turn, this has enabled the snapshot distance method (SDM; Stahl et al 2021), which allows SN Ia distances to be estimated with as little as one spectrum and two photometric points in different passbands. For the first time ever, we use the SDM to "resurrect" a significant sample of SNe Ia which would otherwise have to be discarded in cosmological studies, and we include this sample in our analysis.…”