STag: Supernova Tagging and Classification

TL;DR

STag introduces a machine learning approach using multi-label classification to analyze supernova spectra, enabling efficient tagging and classification without relying solely on traditional class definitions.

Contribution

The paper presents STag, a novel software that applies logistic regression and neural networks for spectral tagging and classification of supernovae, accommodating increasing diversity.

Findings

Effective spectral tagging with probability measures

Accurate classification into standard supernova classes

Flexible handling of diverse supernova spectral features

Abstract

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.