Photometric detection of internal gravity waves in upper main-sequence stars: III. Comparison of amplitude spectrum fitting and Gaussian process regression using CELERITE2

Context. Recent studies of massive stars using high-precision space photometry have revealed that they commonly exhibit stochastic low-frequency (SLF) variability. This has been interpreted as being caused by internal gravity waves excited at the interface of convective and radiative regions within stellar interiors, such as the convective core or sub-surface convection zones, or being caused by dynamic turbulence associated with sub-surface convection zones within the envelopes of main-sequence massive stars. Aims. We aim to compare the properties of SLF variability in massive main-sequence stars observed by the Transiting Exoplanet Survey Satellite (TESS) mission determined by different statistical methods, and confirm the correlation between the morphology of SLF variability and a star's location in the Hertzsprung-Russell (HR) diagram. We also aim to quantify the impact of data quality on the inferred SLF morphologies using both fitting methodologies. Methods. From a sample of 30 previously observed and characterised galactic 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 simple harmonic oscillator (SHO) kernel. Results. We find a difference in the characteristic frequency obtained from the amplitude spectrum fitting and from fitting the covariance 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. We also find that the method of GP regression is more efficient in terms of computation time and, on average, more robust against the quality and noise properties of the input time series data in determining the properties of SLF variability. Conclusions. 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.

Automated summary

Recent studies have shown that massive stars commonly exhibit stochastic low-frequency variability, which is believed to be caused by internal gravity waves or dynamic turbulence. This study aims to compare the properties of this variability and confirm its correlation with a star's location in the HR diagram using different statistical methods. The results suggest that Gaussian process regression is an efficient and novel methodology for characterizing stochastic low-frequency variability in massive stars.