Forward modelling and the quest for mode identification in rapidly-rotating stars
Giovanni Marcello Mirouh

TL;DR
This paper reviews the challenges and recent advances in mode identification in rapidly rotating stars, emphasizing the role of forward modelling, pattern analysis, and machine learning to interpret complex oscillation spectra.
Contribution
It provides a comprehensive overview of mode geometries, frequency patterns, and new techniques like machine learning to improve seismic modelling of rapid rotators.
Findings
Progress in deciphering gravity-mode spectra
Development of machine-learning classification methods
Enhanced forward seismic modelling for rapid rotators
Abstract
Asteroseismology has opened a window on the internal physics of thousands of stars, by relating oscillation spectra properties to the internal physics of stars. Mode identification, namely the process of associating a measured oscillation frequency to the corresponding mode geometry and properties, is the cornerstone of this analysis of seismic spectra. In rapidly rotating stars this identification is a challenging task that remains incomplete, as modes assume complex geometries and regular patterns in frequencies get scrambled under the influence of the Coriolis force and centrifugal flattening. In this article, I will first discuss the various classes of mode geometries that emerge in rapidly-rotating stars and the related frequency and period patterns, as predicted by ray dynamics, complete (non-)adiabatic calculations, or using the traditional approximation of rotation. These…
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Taxonomy
TopicsInertial Sensor and Navigation · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
