within the target aperture for each image.

The light curves produced by this method clearly show instrumental noise due to the varying position of the star on the detector. We used the [k2sc]{} algorithm [@2016MNRAS.459.2408A] to remove this instrumental noise. This algorithm uses Gaussian processes to decompose the light curve into a trend associated with the position of the star on the detector plus a trend with time that represents the intrinsic variability of the star. We first detrend the data using a squared-exponential kernel to describe the covariance properties of the trend with time. This kernel is suitable for smooth, aperiodic variations so we mask the eclipses for this calculation. We then use a Lomb-Scargle periodogram [@1992nrfa.book.....P] to characterise any periodic or quasi-periodic variability in the detrended light curve between the eclipses. This variability can be due to modulation of the light curve by star spots on one or both stars, or due to pulsations. The periods that we judged to be significant detected by this process are noted in Table \[lcinfo\] and are listed in order of power from strongest to weakest. For the stars whose period is noted in Table \[lcinfo\] we repeated the detrending using a 