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.
@article{arxiv.2108.10497,
title = {STag: Supernova Tagging and Classification},
author = {William Davison and David Parkinson and Brad E. Tucker},
journal= {arXiv preprint arXiv:2108.10497},
year = {2022}
}
Comments
20 pages, 9 figures. Pages 13-20 are long tables. The code can be found at https://github.com/wdavison909/STag