[onhn]

> astronomers use supernovae to measure distances, which is important for cosmologists to study, for instance, the expansion of the universe and dark energy.

AFAIK supernovae datasets are usually obtained as a survey, resulting in a statistical sample which can be used to compute cosmological observables. Here it seems that there is a new sample bias introduced by the neural network classifier. Can this bias be accurately quantified?

[gammarator]

A perceptive question. Typically, this bias is assessed by injecting fake supernovae into the images and seeing if they are correctly recovered.