# SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification

## TL;DR

SuperNNova is an open-source deep learning framework using recurrent neural networks for accurate, scalable, and robust supernova classification based solely on light-curve data, with optional host-galaxy redshift information.

## Contribution

It introduces a Bayesian RNN-based framework that achieves high accuracy in supernova classification without feature engineering and addresses calibration and uncertainty estimation issues.

## Key findings

- Achieves >96.92% accuracy without redshift, >99.55% with redshift.
- Classifies incomplete light-curves with >86.4% accuracy two days before maximum light.
- Demonstrates improved uncertainty handling and robustness over traditional methods.

## Abstract

We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves using photometric information only. Additional information such as host-galaxy redshift can be incorporated to improve performance. We evaluate our framework using realistic supernovae simulations that include survey detection. We show that our method, for the type Ia vs. non Ia supernovae classification problem, reaches accuracies greater than 96.92 +- 0.09 without any redshift information and up to 99.55 +- 0.06 when redshift, either photometric or spectroscopic, is available. Further, we show that our method attains unprecedented performance for classification of incomplete light-curves, reaching accuracies >86.4 +- 0.1 (>93.5 +- 0.8) without host-galaxy redshift (with redshift information) two days before maximum light. In contrast with previous methods, there is no need for time-consuming feature engineering and we show that our method scales to very large datasets with a modest computing budget. In addition, we investigate often neglected pitfalls of machine learning algorithms. We show that commonly used algorithms suffer from poor calibration and overconfidence on out-of-distribution samples when applied to supernovae data. We devise extensive tests to estimate the robustness of classifiers and cast the learning procedure under a Bayesian light, demonstrating a much better handling of uncertainties. We study the benefits of Bayesian RNNs for SN Ia cosmology. Our code is open-sourced and available on https://github.com/supernnova/SuperNNova.