STag II: Classification of Serendipitous Supernovae Observed by Galaxy Redshift Surveys

TL;DR

This paper introduces STag II, an improved machine learning-based classifier for supernova spectra that leverages model and real survey data, enhancing accuracy and robustness for upcoming large-scale astronomical surveys.

Contribution

STag II advances supernova classification by integrating model spectra with real survey data and employing the rlap score for improved reliability.

Findings

Enhanced classification accuracy with real survey data

Robustness improved through rlap score filtering

Better handling of realistic supernova spectra

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.