amination Ratio

[Figure: Figure 5. Top: Ratio of the measured 1-hour noise-to-signal ratios to those predicted by the model of Cabrera et al. (2026), shown as a function of the contamination ratio. The stars observed by 6, 12, 18, and 24 cameras are denoted by triangles, circles, squares, and diamonds, respectively. Bottom: Noise redness, 𝛽1hr, as defined in Eq. 1, shown as a function of the contamination ratio (the mean ratio of contaminant to target flux in the photometric apertures).]

𝛽1hr = 12𝜎1hr
, (1)
𝜎25s where a value 𝛽1hr = 1 is consistent with the expectation for whitenoise. A plot of 𝛽1hr against host brightness for each star and for each photometry method is shown in panel (e) of Fig. 3. The median 𝛽1hr across the sample is 1.063 and the 90th percentile value is 1.297, suggesting that most stars are dominated by white noise up to 1-hr timescales, which implies that the out-oftransit variability is slow enough to be captured by the 1-day window detrending. There are some significant outliers (nine stars with 𝛽1hr > 1.5). We find that the best predictor of non-white noise (a large 𝛽1hr) is simply the flux contamination ratio. This is illustrated in the lower panel of Fig. 5. Similar to the noise excess, significant red noise appears to occur at contamination ratios ≳0.5, although substantial variation in 𝛽1hr remains even at large contamination ratios. Additionally, we investigated the impact of stellar variability on the measured noise redness. Using the detrended, injected intrinsic variability described in Section 3.2, we calculated a stellar-only 𝛽1hr value for each star. The resulting 16th, 50th, and 84th percentiles over the stellar sample are [3.30, 3.64, 4.39], indicating that the intrinsic stellar variability is strongly red. However, for this redness to measurably affect the extracted photometry, the stellar contribution must also constitute a significant fraction of the total noise budget. To estimate the contribution from the instrument alone, we derived an instrumental-only 𝛽1hr for each star by subtracting the stellar noise in quadrature from the total noise at both the 25-second and 1-hour cadences. We find that the instrumental-only 𝛽1hr is only significantly closer to unity than the total 𝛽1hr – which we define as 𝛽1hr −1 decreasing by at least a factor of two – for four stars, all brighter than 𝑉= 12 mag. The median instrumental-only redness is 1.059, only marginally smaller than the median value for the total noise. This suggests that, although stellar variability is intrinsically highly red, it contributes only weakly to the overall red noise budget for most stars in the sample. Consequently, the observed redness of the extracted photometry is dominated primarily by instrumental systematics except for the brightest targets, where the stellar contribution becomes appreciable.
the flux contamination ratios (shown in the upper panel of Fig. 5) with a turn-on at ratios ≳0.5. We note that the quick noise model of Cabrera et al. (2026) assumes a fixed photometric aperture size. However, as shown in panel (d) of Fig. 3, the photometric-precision-optimizing aperture selection method of Marchiori et al. (2019) naturally selects smaller apertures for dimmer stars. One might therefore expect the model to overestimate the noise levels of faint targets, since smaller apertures include smaller contributions from sky background and read noise. In practice, however, this effect is not clearly observed, likely because any reduction in read noise and sky-background noise is offset by lower integrated flux and the generally higher contamination levels associated with fainter stars. Fig. 6 shows the distribution of the relative noise performance of PLATO and Kepler for the sample of host stars. The Kepler noise levels were derived in largely the same manner as for the PLATO light curves: by detrending, where possible, the entire available shortcadence data and measuring the 3𝜎-clipped 1-hour scatter. Longcadence data were used when short-cadence data were unavailable. The median relative noise performance scales as expected with the inverse square root of the number of cameras, ranging from 2.68 times the Kepler noise for 24 cameras to 5.14 times the noise for six cameras.