Unsupervised clustering of Type II supernova light curves

Understand

As new facilities come online, the astronomical community will be provided with extremely large datasets of well-sampled light curves (LCs) of transient objects.

• This motivates systematic studies of the light curves of supernovae (SNe) of all types, including the early rising phase.
• We performed unsupervised k-means clustering on a sample of 59 R-band Type II SN light curves and find that our sample can be divided into three classes: slowly-rising (II-S), fast-rise/slow-decline (II-FS), and fast-rise/fast-decline (II-FF).
• We also identify three outliers based on the algorithm.