Photometric detection of internal gravity waves in upper main-sequence stars

Abstract

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 (IGWs) excited at the interface of convective and radiative regions within stellar interiors, such as the convective core or sub-surface convection zones, or caused by dynamic turbulence excited in the 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 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. 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.

Variability of Blue Supergiants in the LMC with TESS

“…The high-frequency tail above the peak frequency in our signal looks very similar to an already widely discussed characteristic signal in hot massive stars, commonly referred to as the SLF variability or red noise (Bowman et al 2019b(Bowman et al , 2019a(Bowman et al , 2020Szewczuk et al 2021;Bowman & Dorn-Wallenstein 2022;Dorn-Wallenstein et al 2022). We note that, however, the signal pattern in our sample is distinct as it has decaying power toward zero frequency, while the red noise signal mostly concerns the high-frequency tail.…”

The photometric variability of massive stars due to gravity waves excited by core convection

“…While we have shown the photometric variability from convectively excited gravity waves is not directly detectable, these waves may mix chemicals 46 or transport angular momentum 26 , leading to observable signals in more traditional g-mode asteroseismology 9 , 47 , 48 . Even though the red noise signal is not the surface manifestation of gravity waves, it still carries valuable information about both the near-surface structure of massive stars as well as their masses and ages 15 , 18 .…”

“…It was recently shown that photometric light curves of hot, massive stars contain a ubiquitous red noise signal 13 – 18 . Theories for the driving mechanism of red noise include gravity waves stochastically excited by core convection, or turbulence from subsurface convection zones 14 , 19 , 20 .…”

Variability of Magnetic Hot Stars from the TESS Observations

“…Although two-and three-dimensional hydrodynamical simulations of IGWs for massive stars with M > 8 M e have also been done (e.g., Herwig et al 2023;Ratnasingam et al 2023;Thompson et al 2023), the influence of magnetic fields on these stars is still not widely studied. Stochastic low-frequency (SLF) variability is observed in upper-mainsequence stars (e.g., Bowman et al 2019aBowman et al , 2019bBowman et al , 2020Bowman & Dorn-Wallenstein 2022). SLF variability is seen across a wide range in mass, age, and metallicity in massive stars (Bowman et al 2020(Bowman et al , 2019b.…”

“…The origin of SLF variability is currently under debate. It could be caused by the IGWs (e.g., Bowman et al 2019aBowman et al , 2020Edelmann et al 2019;Bowman & Dorn-Wallenstein 2022), or by subsurface convection zones (e.g., Blomme et al 2011;Cantiello & Braithwaite 2019;Lecoanet et al 2019;Cantiello et al 2021). Bowman et al (2019b) investigated the 167 OB-type stars within the Milky Way and Large Magellanic Cloud, and reported that SLF variability is insensitive to the metallicity of the star.…”