STag: Supernova Tagging and Classification
  Supernovae classes have been defined phenomenologically, based on spectral
features and time series data, since the specific details of the physics of the
different explosions remain unrevealed. However, the number of these classes is
increasing as objects with new features are observed, and the next generation
of large-surveys will only bring more variety to our attention. We apply the
machine learning technique of multi-label classification to the spectra of
supernovae. By measuring the probabilities of specific features or `tags' in
the supernova spectra, we can compress the information from a specific object
down to that suitable for a human or database scan, without the need to
directly assign to a reductive `class'. We use logistic regression to assign
tag probabilities, and then a feed-forward neural network to filter the objects
into the standard set of classes, based solely on the tag probabilities. We
present STag, a software package that can compute these tag probabilities and
make spectral classifications.
