Unsupervised clustering of Type II supernova light curves

Abstract

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.