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Oscillation modes in fast-rotating stars can be split into severalsubclasses, each with their own properties. To date, seismology of these starscannot rely on regular pattern analysis and scaling relations. However,recently there has been the promising discovery of large separations observedin spectra of fast-rotating $\delta$ Scuti stars: they were attributed to theisland-mode subclass, and linked to the stellar mean density through a scalinglaw. In this work, we investigate the relevance of this scaling relation bycomputing models of fast-rotating stars and their oscillation spectra. In orderto sort the thousands of oscillation modes thus obtained, we train aconvolutional neural network isolating the island modes with 96\% accuracy.Arguing that the observed large separation is systematically smaller than theasymptotic one, we retrieve the observational $\Delta\nu - \overline{\rho}$scaling law. This relation will be used to drive forward modelling efforts, andis a first step towards mode identification and inversions for fast-rotatingstars.

Mode Classification in Fast-Rotating Stars using a Convolutional Neural Network: Model-Based Regular Patterns in $\delta$ Scuti Stars