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

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