are highly significant. A variable, sinusoidal model is very strongly preferred for and , while a non-variable, flat model is very strongly favored for . These results are fully consistent with the periodogram method for identifying variability discussed in Section \[sec:var\_id\].]{}

Determining rotation periods using MCMC
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To determine the rotation period and variability amplitude of our variable objects we use the MCMC algorithm [@fm2013] to fit a sinusoidal model to the data in each band for and . Both variable objects exhibit fairly uniform, sinusoidal light curves and thus do not warrant a Fourier model fit with additional parameters [e.g @Vos2018]. For the MCMC analysis we use 1000 walkers with 10000 steps. We discard the initial 1000 steps as the burn-in sample. We check for convergence by visually inspecting the resulting chains for each parameter to check that they are consistent with random noise of constant mean and variance. We also check that there is no difference between the parameter constraints obtained from the first and second halves of the chain. Based on these two checks, we find that the MCMC converges well for each sinusoidal model fit.

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  Target        SpT          Channel   Amplitude 