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tokens: as done for the RMS . For the following analysis, we used these mean P2P RMS trends to identify, for each star, the best photometry: for each star in the input catalogue with a given T magnitude, we selected the light curve obtained with the photometric method that at the given T returns the lower mean P2P RMS . In this phase we also excluded all the light curves that have 300 photometric points and whose mean magnitude differs from that expected from the input catalogue >2 magnitudes. Panel (c) of Fig. 6 shows the 6.5-h RMS distribution (gray points) for the previously selected light curves. This transit noise 6.5-h RMS is obtained as described by Gilliland et al. ( 2011 ), Gilliland et al. ( 2015 ), and Van Cleve et al. ( 2016 ): we flattened each light curve interpolating to it a 5th-order spline with 65 break points and removing out the outliers. We computed the standard deviation of a running mean with a window length equal to 6.5 h (13 points). The azure line in panel (c) is the mean trend of the 6.5-h RMS distribution, calculated as previously described: the trend reaches a minimum at ∼34 ppm. For T 10, the distribution is bi-modal: stars that are not variable or whose period variability is >6.5 h have 6.5-h RMS ∼ 20 ppm. The stars with 6.5-h RMS ≳ 30 are highly variable on short time-scales and the flattening is not perfect. For stars with 10 ≲ T ≲ 14, we are able to detect exoplanets transits with depth 1 mmag, while for T ≳ 14 it is possible to find transits with depth 0.01 mag. 3 CANDIDATE EXOPLANET TRANSIT FINDING We searched for transiting signals among the light curves selected in the previous section. As described in Section 2.5 , we flattened all the light curves using 5th order splines defined on 65 knots. For each light curve, we removed out the outliers and the photometric points with 1347.4 t BTJD 1349.4, whose photometry is affected by pointing problems and that, despite the cotrending, still have some residual systematic artifacts. For each light curve, we extracted both the Box-fitting Least-Squares (BLS, Kovács, Zucker Mazeh 2002 ) and Transit-fitting Least Squares (TLS, Hippke Heller 2019 ) periodograms. For both techniques we searched for transiting objects having period 0.6 ≤ P ≤ 14 d, and for each star we extracted the parameters useful to discriminate between light curves with and without candidate transits, such as the signal detection efficiency (SDE), the signal-to-noise ratio (SNR), the depth of the transit. To detect candidate transits we performed two independent analysis on BLS and TLS outputs and at the end we joined the results. In Fig. 7 , we summarize the procedure adopted for the selection of candidate transiting exoplanets both for TLS (left-hand panels) and BLS (right-hand panels) technique. As described in Nardiello et al. ( 2015 ), we excluded spurious periods due to systematic errors by constructing the histogram of the period and removing the stars that form the spikes in the histogram: the red