Open source photometric classification

SuperNNova is an open source photometric classification framework. It uses deep learning Recurrent Neural Networks to classify photometric time-series (light-curves).

It does not require feature extraction, is fast and is adaptable to different transients and survey. When trained with large simulations using the observing strategy of a survey, it accurately classifies events and has been applied to data from the Dark Energy Survey (publication) and Zwicky Transient Facility (publication).

With SuperNNova I introduced the use of Bayesian Neural Networks in supernova classification. We aim to use these networks to quantify uncertainties and use them in astrophysical analyses!

SuperNNova publication MNRAS (Möller et al. 2020)

SuperNNova publication ArXiv (open source)

SuperNNova on GitHub