Improved Photometric Classification of Supernovae using Deep Learning

Understand

We present improved photometric supernovae classification using deep recurrent neural networks.

• The main improvements over previous work are (i) the introduction of a time gate in the recurrent cell that uses the observational time as an input; (ii) greatly increased data augmentation including time translation, addition of Gaussian noise and early truncation of the lightcurve.
• For post Supernovae Photometric Classification Challenge (SPCC) data, using a training fraction of $5.2\%$ (1103 supernovae) of a representational dataset, we obtain a type Ia vs.
• non type Ia classification accuracy of $93.2 \pm 0.1\%$, a Receiver Operating Characteristic curve AUC of $0.980 \pm 0.002$ and a SPCC figure-of-merit of $F_1=0.57 \pm 0.01$.