additionally use the Bayesian Information Criterion (BIC) to search for variability in each observation, following the method described in @Naud2017. The BIC is defined as]{} $$\mathrm{BIC}=-2~\mathrm{ln}~\mathcal{L}_\mathrm{max} + k ~\mathrm{ln}~ N$$ [where $\mathcal{L}_\mathrm{max}$ is the maximum likelihood achievable by the model, $k$ is the number of parameters in the model and $N$ is the number of datapoints used in the fit [@Schwarz1978]. ]{}

[For each observation, we calculate $\Delta\mathrm{BIC} = \mathrm{BIC}_{sin} - \mathrm{BIC}_{\mathrm{flat}}$ to assess whether the variable sinusoidal or non-variable flat model is favored by the data. The BIC penalizes the sinusoidal model for having additional parameters compared with the flat model. These values are shown in Table \[tab:parameters\]. A negative value of $\Delta\mathrm{BIC}$ indicates that the sinusoidal model is favored and a positive value indicates that the non-variable, flat model is favored. A $|\Delta\mathrm{BIC}|$ value between 0 and 6 indicates that one model is positively favored over the other, a value between 6 and 10 indicates that one model is strongly favored over the other and values above 10 indicate that one model is very strongly favored over the other [@Schwarz1978]. All of the $|\Delta\mathrm{BIC}|$ values shown in Table \[tab:parameters\] are much greater than 10, showing that these results 