DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

We present \texttt{DASH} (Deep Automated Supernova and Host classifier), a novel software package that automates the classification of the type, age, redshift, and host galaxy of supernova spectra. \texttt{DASH} makes use of a new approach which does not rely on iterative template matching techniques like all previous software, but instead classifies based on the learned features of each supernova type and age bin. It has achieved this by employing a deep convolutional neural network to train a matching algorithm. This approach has enabled \texttt{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 the last four years of data from the ongoing Australian Dark Energy Survey (OzDES). The deep learning models were developed using \texttt{TensorFlow}, and were trained using over 4000 supernova templates taken from the CfA Supernova Program and the Berkeley SN Ia Program as used in \texttt{SNID} (Supernova Identification software, Blondin & Tonry (2007)). The trained models are independent of the number of templates, which allows for \texttt{DASH}'s unprecedented speed. We have developed both a graphical interface for easy visual classification and analysis of supernovae, and a \texttt{Python} library for the autonomous and quick classification of several supernova spectra. The speed, accuracy, user-friendliness, and versatility of \texttt{DASH} presents an advancement to existing spectral classification tools. We have made the code publicly available on \texttt{GitHub} and PyPI (\texttt{pip install astrodash}) to allow for further contributions and development. The package documentation is available at \url{https://astrodash.readthedocs.io}.