@misc{10481/111687,
year = {2019},
month = {2},
url = {https://hdl.handle.net/10481/111687},
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 Δ ν - \overline{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.},
organization = {SISSA, Via Bonomea 265, I-34136 Trieste, Italy},
organization = {Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str 1, D-85748 Garching, Germany},
organization = {Astrophysics Research Group, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK},
publisher = {Oxford University Press},
keywords = {stars: oscillations},
keywords = {stars: rotation},
keywords = {stars:variables:delta Scuti},
title = {Mode classification in fast-rotating stars using a convolutional neural network: model-based regular patterns in δ Scuti stars},
doi = {10.1093/mnrasl/sly212},
author = {Mirouh, Giovanni Marcello and Angelou, George C. and Reese, Daniel R. and Costa, Guglielmo},
}