DASH: Deep Learning for the Automated Spectral Classification of
  Supernovae and their Hosts
  We present 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. 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 four years 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, Blondin & Tonry 2007). Unlike template matching
methods, the trained models are independent of the number of spectra in the
training data, which allows for DASH's unprecedented speed. We have developed
both a graphical interface for easy visual classification and analysis of
supernovae, and a Python library for the autonomous and quick classification of
several supernova spectra. The speed, accuracy, user-friendliness, and
versatility of DASH presents an advancement to existing spectral classification
tools. We have made the code publicly available on GitHub and PyPI (pip install
astrodash) to allow for further contributions and development. The package
documentation is available at https://astrodash.readthedocs.io.
