Between Plateaus and Slopes: A Data-Driven Exploration of Spectral Diversity Across Type IIP/L Supernovae
arXiv:2602.15788 · doi:10.1051/0004-6361/202556773
Abstract
Type II supernovae (SNe II) have been traditionally separated into several subgroups based on their photometric and spectroscopic properties, but whether these represent distinct progenitors or a continuous distribution remains debated. Over the past decade, growing observational evidence has suggested a possible continuity between slow- (IIP) and fast-declining (IIL) SNe. We investigate the continuity of the SNe IIP/L subclasses through a data-driven statistical analysis of spectral time series, aiming to determine whether significant correlations exist between overall spectral shapes and light-curve decline rates. We introduce a novel standardization method for SN II spectra. After empirically flattening the spectra via continuum normalization, we interpolate the resulting "feature spectra" onto a fixed grid of epochs using Gaussian Process regression. The interpolated spectra are then analyzed using Principal Component Analysis to explore correlations. We find that SNe IIP and IIL form a continuum spectroscopically, though some clustering remains. The spectral diversity is characterized mainly by two components: one continuous group with well-defined P-Cygni profiles and another with "less-regular" features likely driven by enhanced circumstellar material (CSM) interaction. Our results reveal that the spectral diversity of SNe IIP/L diminishes over time. We confirm observational correlations: steeper light-curve declines correspond to weaker spectral features, indicating that SNe IIL tend to show weaker emission and, in some cases, a lack of distinct absorption lines. These trends seemingly break down by enhanced CSM interaction that affects the P-Cygni profiles. Our data-driven method reveals underlying spectral correlations and supports a continuous distribution between IIP and IIL subtypes. This method paves the way for more refined classification algorithms.
13+7 pages main text and appendix, 13 figures, accepted for publication in A&A
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