SCONE (Supernova Classification with a Convolutional Neural Network) classifies supernovae (SNe) by type using multi-band photometry data (lightcurves) using a convolutional neural networks. SCONE takes in supernova (SN) photometry data in the format output by SNANA simulations, separated into two types of files: metadata and observation data. Photometric data is pre-processed via 2D Gaussian process regression, which smooths over irregular sampling rates between filters and also allows SCONE to be independent of the filter set on which it was trained.

SNANA is a general analysis package for supernova (SN) light curves that contains a simulation, light curve fitter, and cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe.