the three methods described in Section \[sec\_per\] on the [artificial]{} data and analyze the periodicity. For sinusoidal fitting, we again bin the light curve every 300 days. We identify a “periodic candidate” if the following criteria are satisfied:

1.  the LS period of the [artificial]{} data is equal to or smaller than that of the observed data, to ensure that the [artificial]{} data covers as many cycles of the period as the observed data;

2.  the peak power of the LS periodogram is larger than or equal to that of the observed data ($P_{\rm LS}\geqslant0.68$) and the minimum of the PDM periodogram is smaller than or equal to that of the observed data ($P_{\rm PDM}\leqslant0.41$);

3.  a sinusoid improves the goodness of fit compared to a constant with $\Delta \chi^2$ greater than or equal to that of the binned observed data ($\Delta \chi^2 \geqslant 150$; see Section 4.1);

4.  the LS period agrees with the periods from PDM analysis and sinusoidal fitting at a level of $\pm 1.0$ years.

The choice of the second criterion is for the purpose of selecting light curves with strong periodic variation amplitudes; the third criterion is to largely ensure roughly equal fluxes of the 