correlate with the corrected flux of our variable and non-variable targets.]{}

![image](full_lc_periodogram_2M0045+16-eps-converted-to.pdf)

![image](full_lc_periodogram_2M0501-00-eps-converted-to.pdf)

![image](full_lc_periodogram_2M1425-36-eps-converted-to.pdf)

Identification of Variables {#sec:var_id}
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We search for variability in our observed light curves using the periodogram analysis outlined in @Vos2018 and using a Bayesian framework outlined in @Naud2017.

Variability Detection with Periodogram Analysis
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We calculate the Lomb-Scargle periodograms of our target and reference star light curves [@Scargle1982] to assess the significance of their trends. For each observation we calculate the $95\%$ and $99\%$ significance thresholds by simulating 1000 light curves from our observed reference stars. We create the simulated light curves by randomly rearranging the indices of the reference star light curves, which produces simulated light curves with Gaussian noise equal to that of our observed light curves. The $95\%$ and $99\%$ significance thresholds are plotted in blue in the bottom panels of Figures \[fig:2M0045\_full\_lc\] - \[fig:2M1425\_full\_lc\]. The periodograms of targets , (Figures \[fig:2M0045\_full\_lc\] and \[fig:2M0501\_full\_lc\]) display power that is significantly above the threshold at both $3.6~\mu$m and $4.5~\mu$m. The periodograms for each target peak at roughly the same period in both channels, which further supports that the variation is rotationally modulated. Our final target , shown in Figure \[fig:2M1425\_full\_lc\] does not exhibit periodogram 