On the cosmological performance of photometrically classified supernovae with machine learning

Understand

The efficient classification of different types of supernova is one of the most important problems for observational cosmology.

• However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible.
• The development of automated classification processes based on photometry has thus become crucial.
• In this paper we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classification Challenge, released in 2010.