Unsupervised clustering of Type II supernova light curves
  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. We find that performing
clustering on the first two components of a principal component analysis gives
equivalent results to the analysis using the full LC morphologies. This may
indicate that Type II LCs could possibly be reduced to two parameters. We
present several important caveats to the technique, and find that the division
into these classes is not fully robust and is sensitive to the uncertainty on
the time of first light. Moreover these classes have some overlap, and are
defined in the R-band only. It is currently unclear if they represent distinct
physical classes, and more data is needed to study these issues. However, our
analysis shows that the outliers are actually composed of slowly-evolving SN
IIb, demonstrating the potential use of such methods. The slowly-evolving SNe
IIb may arise from single massive progenitors.
