Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning

Understand

We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification.

• The strategy accounts for the unavoidable mismatch between spectroscopic and photometric samples, and can be used even in the beginning of a new survey -- without any initial training set.
• The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in improving the classifier predictions.
• As a proof of concept, we use the simulated data released after the Supernova Photometric Classification Challenge (SNPCC) and a random forest classifier.