Mode classification in fast-rotating stars using a convolutional neural 
network: model-based regular patterns in δ Scuti stars

Mirouh, Giovanni M.; Angelou, George C.; Reese, Daniel R.; Costa, Guglielmo

doi:10.1093/mnrasl/sly212

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Mode classification in fast-rotating stars using a convolutional neural network: model-based regular patterns in δ Scuti stars

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Mirouh, Giovanni M.
Stellar Evolution, Stellar Astrophysics, MPI for Astrophysics, Max Planck Society;

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Angelou, George C.
Max Planck Research Group in Stellar Ages and Galactic Evolution (SAGE), Max Planck Institute for Solar System Research, Max Planck Society;

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Citation

Mirouh, G. M., Angelou, G. C., Reese, D. R., & Costa, G. (2018). Mode classification in fast-rotating stars using a convolutional neural network: model-based regular patterns in δ Scuti stars. Monthly Notices of the Royal Astronomical Society, 483(1), L28-L32. doi:10.1093/mnrasl/sly212.

Cite as: https://hdl.handle.net/21.11116/0000-0004-B386-A

Abstract

Oscillation modes in fast-rotating stars can be split into several subclasses, each with their own properties. To date, seismology of these stars cannot rely on regular pattern analysis and scaling relations. However, recently there has been the promising discovery of large separations observed in spectra of fast-rotating δ Scuti stars; they were attributed to the island-mode subclass, and linked to the stellar mean density through a scaling law. In this work, we investigate the relevance of this scaling relation by computing models of fast-rotating stars and their oscillation spectra. In order to sort the thousands of oscillation modes thus obtained, we train a convolutional neural network isolating the island modes with 96 per cent accuracy. Arguing that the observed large separation is systematically smaller than the asymptotic one, we retrieve the observational Δν--ρ¯ scaling law. This relation will be used to drive forward modelling efforts, and is a first step towards mode identification and inversions for fast-rotating stars.