Automated DASH classification for supernovae

DASH makes use of a new approach that does not rely on iterative template-matching techniques like all previous software, but instead classifies based on the learned features of each supernova's type and age. It has achieved this by employing a deep convolutional neural network to train a matching algorithm.

This approach has enabled DASH to be orders of magnitude faster than previous tools, being able to accurately classify hundreds or thousands of objects within seconds. We have tested its performance on 4yr of data from the Australian Dark Energy Survey (OzDES). The deep learning models were developed using TensorFlow and were trained using over 4000 supernova spectra taken from the CfA Supernova Program and the Berkeley SN Ia Program as used in SNID (Supernova Identification software).