The snapshot distance method: estimating the distance to a Type Ia supernova from minimal observations
Benjamin E. Stahl, Thomas de Jaeger, WeiKang Zheng, Alexei V., Filippenko

TL;DR
The paper introduces the snapshot distance method (SDM), a new technique that estimates the distance to Type Ia supernovae using minimal observations, leveraging deep learning for rapid and robust distance measurements.
Contribution
The paper presents the SDM, a novel approach that estimates supernova distances from a single spectrum and minimal photometry, demonstrating robustness across various observational conditions.
Findings
Median residual between SDM and traditional distances is 0.013 mag.
SDM is robust to different spectral and photometric sampling.
Time of maximum brightness minimally affects distance estimates.
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 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…
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