Photometric detection of internal gravity waves in upper main-sequence stars. III. Comparison of amplitude spectrum fitting and Gaussian process regression using celerite2

TL;DR

This study compares amplitude spectrum fitting and Gaussian process regression methods to analyze stochastic low-frequency variability caused by internal gravity waves in massive main-sequence stars, confirming its correlation with stellar position in the HR diagram.

Contribution

It introduces the use of celerite2 Gaussian process regression for characterizing SLF variability, demonstrating its effectiveness and robustness compared to traditional spectral fitting methods.

Findings

GP regression provides consistent characteristic frequency estimates

SLF variability correlates with star's HR diagram position

Distribution in stochasticity relates to mass and age

Abstract

Studies of massive stars using space photometry have revealed that they commonly exhibit stochastic low-frequency (SLF) variability. This has been interpreted as being caused by internal gravity waves (IGWs) excited at the interface of convective and radiative regions within stellar interiors, such as the convective core or sub-surface convection zones. We aim to compare the properties of SLF variability in massive main-sequence stars observed by the TESS mission determined by different statistical methods, and confirm the correlation between the morphology of SLF variability and a star's location in the HR diagram. From a sample of 30 massive stars observed by TESS, we compare the resultant parameters of SLF variability, in particular the characteristic frequency, obtained from fitting the amplitude spectrum of the light curve with those inferred from fitting the covariance structure of the light curve using the celerite2 Gaussian process (GP) regression software and a damped SHO kernel. We find a difference in the characteristic frequency obtained from the amplitude spectrum fitting and from fitting the co-variance structure of the light curve using a GP regression with celerite2 for only a minority of the considered sample. However, the trends among mass, age and the properties of SLF variability previously reported remain unaffected. GP regression is a useful and novel methodology to efficiently characterise SLF variability in massive stars compared to previous techniques used in the literature. We conclude that the correlation between a star's SLF variability, in particular the characteristic frequency, and its location in the HR diagram is robust for main-sequence massive stars. There also exists a distribution in the stochasticity of SLF variability in massive stars, which indicates that the coherency of SLF variability is also a function of mass and age in massive stars.