Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification

Understand

In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects.

• Our focus here is the classification of supernovae (SNe).
• We consider random forest machine learning algorithm applied to the classification of Type Ia and core-collapse supernovae (CCSNe) by full light curves.
• We also quantitatively show the potential of early-epoch classification using the same machine learning techniques.