Self-consistent method to extract non-linearities from pulsating star light curves – I. Combination frequencies. Issue 1 (6th August 2020)

• Record Type: Journal Article
• Authors: Lares-Martiz, M; Garrido, R; Pascual-Granado, J
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 498:Issue 1(2020)
• Page Start: 1194
• Page End: 1204
• Publication Date: 2020-08-06
• Subjects: asteroseismology -- stars: oscillations -- stars: variables: δ Scuti; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa2256
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Stellar pulsation is a common phenomenon and is sustained because of coherent driving mechanisms. When pulsations are driven by heat or convective mechanisms, it is usual to observe combination frequencies in the power spectra of the stellar light-curves. These combination frequencies are not solutions of the perturbed stellar structure equations. In dense power spectra from a light-curve of a given multiperiodic pulsating star, they can compromise the mode identification in asteroseismic analyses, and hence they must be treated as spurious frequencies and removed. In this paper, a method based on fitting the set of frequencies that best describes a general non-linear model, like the Volterra series, is presented. The method allows these frequencies to be extracted from the power spectrum, thereby improving the frequency analysis and enabling hidden frequencies to emerge from what was initially considered as noise. Moreover, the method yields frequencies with uncertainties several orders of magnitude smaller than the Rayleigh dispersion, which is sometimes used as if it were an error when identifying combination frequencies. Furthermore, it is compatible with the classical counting cycles method, the so-called O-C method, which is valid only for mono-periodic stars. The method creates the possibility of characterizing the non-linear behaviour of a given pulsating star by studying in detail the complex generalized transfer functions on which the model is based.