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

Autor: George C. Angelou, Daniel R. Reese, Guglielmo Costa, G. M. Mirouh
Přispěvatelé: Laboratoire d'études spatiales et d'instrumentation en astrophysique (LESIA (UMR_8109)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Monthly Notices of the Royal Astronomical Society: Letters
Monthly Notices of the Royal Astronomical Society: Letters, Oxford Journals, 2019, 483 (1), pp.L28-L32. ⟨10.1093/mnrasl/sly212⟩
ISSN: 1745-3933
DOI: 10.1093/mnrasl/sly212⟩
Popis: 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 $\delta$ 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\% accuracy. Arguing that the observed large separation is systematically smaller than the asymptotic one, we retrieve the observational $\Delta\nu - \overline{\rho}$ 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.
Comment: 6 pages, 6 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society Letters
Databáze: OpenAIRE