STag II: Classification of Serendipitous Supernovae Observed by Galaxy Redshift Surveys
Abstract
With the number of supernovae observed expected to drastically increase thanks to large-scale surveys like the Dark Energy Spectroscopic Instrument (DESI), it is necessary that the tools we use to classify these objects keep up with this increase. We previously created Supernova Tagging and Classification (STag) to address this problem by employing machine learning techniques alongside logistic regression in order to assign 'tags' to spectra based on spectral features. STag II is a continuation of this work, which now makes use of model supernova spectra combined with real DESI spectra in order to train STag to better deal with realistic data. We also make use of the rlap score as a trustworthiness cut, making for a more robust and accurate supernova classifier than before.
Cite
@article{arxiv.2406.17204,
title = {STag II: Classification of Serendipitous Supernovae Observed by Galaxy Redshift Surveys},
author = {W. Davison and D. Parkinson and S. BenZvi and A. Palmese and J. Aguilar and S. Ahlen and D. Brooks and T. Claybaugh and A. de la Macorra and Arjun Dey and P. Doel and E. Gaztañaga and S. Gontcho A Gontcho and C. Howlett and S. Juneau and T. Kisner and A. Kremin and A. Lambert and M. Landriau and L. Le Guillou and A. Meisner and R. Miquel and J. Moustakas and A. D. Myers and C. Poppett and F. Prada and M. Rezaie and G. Rossi and E. Sanchez and E. F. Schlafly and M. Schubnell and D. Sprayberry and G. Tarlé and B. A. Weaver and H. Zou},
journal= {arXiv preprint arXiv:2406.17204},
year = {2025}
}
Comments
21 pages, 7 figures. The STag code is available at https://github.com/wdavison909/STag