TY - JOUR
T1 - STag
T2 - Supernova Tagging and Classification
AU - Davison, William
AU - Parkinson, David
AU - Tucker, Brad E.
N1 - Publisher Copyright:
© 2022. The Author(s). Published by the American Astronomical Society.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85125838612&partnerID=8YFLogxK
U2 - 10.3847/1538-4357/ac3422
DO - 10.3847/1538-4357/ac3422
M3 - Article
SN - 0004-637X
VL - 925
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 2
M1 - 186
ER -