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The multiscale entropy assesses the complexity of a signal across differenttimescales. It originates from the biomedical domain and was recentlysuccessfully used to characterize light curves as part of a supervised machinelearning framework to classify stellar variability. We explore the behavior ofthe multiscale entropy in detail by studying its algorithmic properties in astellar variability context and by linking it with traditional astronomicaltime series analysis methods. We subsequently use the multiscale entropy as thebasis for an interpretable clustering framework that can distinguish hybridpulsators with both p- and g-modes from stars with only p-mode pulsations, suchas $\delta$ Sct stars, or from stars with only g-mode pulsations, such as$\gamma$ Dor stars. We find that the multiscale entropy is a powerful tool forcapturing variability patterns in stellar light curves. The multiscale entropyprovides insights into the pulsation structure of a star and reveals how short-and long-term variability interact with each other based on time-domaininformation only. We also show that the multiscale entropy is correlated to thefrequency content of a stellar signal and in particular to the near-corerotation rates of g-mode pulsators. We find that our new clustering frameworkcan successfully identify the hybrid pulsators with both p- and g-modes in setsof $\delta$ Sct and $\gamma$ Dor stars, respectively. The benefit of ourclustering framework is that it is unsupervised. It therefore does not requirepreviously labeled data and hence is not biased by previous knowledge.

Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators