Semi-supervised Learning for Photometric Supernova Classification

Understand

We present a semi-supervised method for photometric supernova typing.

• Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova.
• We demonstrate that this is an effective method for supernova typing.
• As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods.