were tested as comparison stars but led to more scatter in the differential light curve than division by the single, bright comparison.

Data Analysis
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Light curve fitting with analytic model
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We initially fitted the differential light curves with analytic limb-darkened transit light curves [@MandelAgol] using a Markov Chain Monte Carlo (MCMC) algorithm, implemented through the <span style="font-variant:small-caps;">emcee</span> [@emcee] Python package. A quadratic limb darkening law was used and fit simultaneously with a long time-scale trend so as not to bias the derived radii. In the red and green channels, this trend was fit with a second order polynomial whilst in the blue it was fit as a function of airmass since it was clearly related to extinction. In order to fit the airmass term in the blue channel, an extinction coefficient that varied quadratically in time was used, which replicated the trend well. An extinction coefficient that varied linearly in time was also tested but could not fit the sharp downturn in the blue light curve at the end of the night (Fig. \[fig:mandel\_agol\]).

The scaled semi major axis, $a/R_{*}$, inclination of the orbit and the time of mid transit, $T_{0}$, were tied across the three light curves 