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tokens: of 5000 simulated white noise time series; d) : LS periodogram of the average of 5000 simulated red noise time series; e) : integrated WWZ power spectrum of 5000 simulated red noise time series; f) : WWZ power spectrum of the average of 5000 simulated red noise time series. Fig. 6 Statistical significance test of WWZ-detected periods in Figs. 2 c and 3 c. The x -axis represents the peak power of simulated periodograms at the periodicity under investigation. The y -axis gives the occurrence number within equally wide bins of 1. Top : histogram obtained from 5000 simulated white noise time series. Bottom : histogram obtained from 5000 simulated red noise time series. The vertical red dashed lines denote the peak powers of the periodic components identified in Figs. 2 and 3 . In the regions to the right of the line, the WWZ power of the simulated fake light curves is higher than the observed value. The ratio of the accumulated count of these light curves to the total number used in the MC test (i.e. 5000) determines the probability of the observed periodicity caused by the noise. As in to Sect. 2.1 , we also evaluated how the noise affected the WWZ power spectrum. The statistical confidence tests were performed using Monte Carlo (MC) simulations ( Linnell Nemec Nemec 1985 ). We first generated 5000 white noise time series, which had the same number of sampling points, temporal baselines, and sampling intervals as the observed light curve. We then calculated the WWZ periodograms for the 5000 white noise time series using the same parameters as for Figs. 2 c and 3 c. The averaged white noise power spectrum is shown in the top of Figs. 4 and 5 . The red noise power spectrum was also obtained in the same way (bottom of Figs. 4 and 5 ). No significant peaks were found at the positions of P1, P2, and P3, in either of the white noise or red noise spectra. For a quantitative evaluation of the statistical confidence, we counted the number of simulated light curves, which show higher power than the observed periodic components (P1, P2, and P3) at their peak positions. The ratio of the accumulated number to the total number of 5000 test light curves gives the probability of the false detection (i.e. the detected peak could be due to random noise). The percentage of the false detections in each case is below 0.1 (see Fig. 6 ). Therefore, the possibility that P1, P2, and P3 are generated by stochastic noise can easily be ruled out. 3. Discussion The periodicity analysis in Sect. 2 results in the detection of three possible periods. The properties of individual periodic components are discussed in the following: 1. P1 = 2.65 d . This is the most prominent pe-riodic component in both the LS periodogram and the WWZpower spectrum, which confirms the first detection by Griséet al. (2013) using the LS tech-nique. The component P1 is remarkable in the first two-thirdsof the interval covered by the light curve. It starts with a slightlylonger period of 2.67 d, then