A graph-based spectral classification of SN-II

This work presents new data-driven classification heuristics for spectral data based on graph theory. As a case in point, we devise a spectral classification scheme of Type II supernova (SNe II) as a function of the phase relative to the V -band maximum light and the end of the plateau phase. Our classification method naturally identifies outliers and arranges the different SNe in terms of their major spectral features. The automated classification naturally reflects the fast evolution of Type II SNe around the maximum light while showcasing their homogeneity close to the end of the plateau phase. The scheme we develop could be more widely applicable to unsupervised time series classification or characterization of other functional data.