Jiří Krtička, Achim Feldmeier

• Context Line-driven wind instability is expected to cause small-scale wind inhomogeneities, X-ray emission, and wind line profile variability. The instability can already develop around the sonic point if it is initiated close to the photosphere due to stochastic turbulent motions. In such cases, it may leave its imprint on the light curve as a result of wind blanketing. Aims We study the photometric signatures of the line-driven wind instability. Methods We used line-driven wind instability simulations to determine the wind variability close to the star. We applied two types of boundary perturbations: a sinusoidal one that enables us to study in detail the development of the instability and a stochastic one given by a Langevin process that provides a more realistic boundary perturbation. We estimated the photometric variability from the resulting mass-flux variations. The variability was simulated assuming that the wind consists of a large number of independent conical wind sectors. We compared the simulated light curvesContext Line-driven wind instability is expected to cause small-scale wind inhomogeneities, X-ray emission, and wind line profile variability. The instability can already develop around the sonic point if it is initiated close to the photosphere due to stochastic turbulent motions. In such cases, it may leave its imprint on the light curve as a result of wind blanketing. Aims We study the photometric signatures of the line-driven wind instability. Methods We used line-driven wind instability simulations to determine the wind variability close to the star. We applied two types of boundary perturbations: a sinusoidal one that enables us to study in detail the development of the instability and a stochastic one given by a Langevin process that provides a more realistic boundary perturbation. We estimated the photometric variability from the resulting mass-flux variations. The variability was simulated assuming that the wind consists of a large number of independent conical wind sectors. We compared the simulated light curves with TESS light curves of OB stars that show stochastic variability. Results We find two typical signatures of line-driven wind instability in photometric data: a knee in the power spectrum of magnitude fluctuations, which appears due to engulfment of small-scale structure by larger structures, and a negative skewness of the distribution of fluctuations, which is the result of spatial dominance of rarefied regions. These features endure even when combining the light curves from independent wind sectors. Conclusions The stochastic photometric variability of OB stars bears certain signatures of the line-driven wind instability. The distribution function of observed photometric data shows negative skewness and the power spectra of a fraction of light curves exhibit a knee. This can be explained as a result of the line-driven wind instability triggered by stochastic base perturbations.…