no code implementations • 22 Dec 2020 • Jonathan E. Carrick, Isobel M. Hook, Elizabeth Swann, Kyle Boone, Chris Frohmaier, Alex G. Kim, Mark Sullivan
Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint, high-redshift supernovae observed from larger spectroscopic facilities; the algorithms' range of average area under ROC curve (AUC) scores over 10 runs increases from 0. 547-0. 628 to 0. 946-0. 969 and purity of the classified sample reaches 95 per cent in all runs for 2 of the 4 algorithms.
Instrumentation and Methods for Astrophysics
