Given the ever-increasing number of time-domain astronomical surveys, employing robust, interpretative, and automated data-driven classification schemes is pivotal. Based on graph theory, we present new data-driven classification heuristics for spectral data. A spectral classification scheme of Type II supernovae (SNe II) is proposed based on the phase relative to the maximum light in the V band and the end of the plateau phase. We utilize a compiled optical data set that comprises 145 SNe and 1595 optical spectra in 4000–9000 Å. Our classification method naturally identifies outliers and arranges the different SNe in terms of their major spectral features. We compare our approach to the off-the-shelf umap manifold learning and show that both strategies are consistent with a continuous variation of spectral types rather than discrete families. 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.

This work is a result of the COIN Residence Program6 (CRP#6) held in Chamonix, France in August 2019. COIN was financially supported by CNRS, France as part of its MOMENTUM programme over the 2018–2020 period. R.S.S. was supported by the National Natural Science Foundation of China project 1201101284. S.T. was supported by the Cambridge Centre for Doctoral Training in Data-Intensive Science, funded by the U.K. Science and Technology Facilities Council (STFC). S.G.G. acknowledges support by the FCT, United States under Project CRISP PTDC/FIS-AST-31546/2017 and Project No. UIDB/00099/2020. L.G. acknowledges financial support from the Spanish Ministerio de Ciencia e Innovación (MCIN), the Agencia Estatal de Investigación (AEI) 10.13039/501100011033, the European Social Fund (ESF) “Investing in your future” under the 2019 Ramón y Cajal program RYC2019-027683-I and the PID2020-115253GA-I00 HOSTFLOWS project, from Centro Superior de Investigaciones Científicas (CSIC) under the PIE project 20215AT016, and the program Unidad de Excelencia María de Maeztu CEX2020-001058-M.

With funding from the Spanish government through the "Severo Ochoa Centre of Excellence" accreditation (CEX2020-001058-M).

Peer reviewed