to the the mean value of $\langle N_\mathrm{3-fold}\rangle=0.048$ (standard deviation $\langle N_\mathrm{3-fold}\rangle^{-1/2}=0.218$), this corresponds to a signal-to-noise-ratio of $SNR>680$. The absolute frequencies for between $4$ and $10$ events per bin indicate pseudo-time-correlated accidental three-fold coincidences.[]{data-label="statistics"}](fig06.eps){width="0.75\linewidth"}

In the histogram in Fig. \[statistics\], we show the result of our statistical analysis. On the x-axis, we plot the number of three-fold coincidences per time-bin during $11.5$ hours of measurement time. The y-axis shows the absolute frequencies of these events’ occurrences. We measured $33$ three-fold coincidences only once with standard deviation of $\sigma_\mathrm{triplet}=5.7$. The noise-related background events are visibly separated and average to $\langle N_\mathrm{3-fold}\rangle=0.048$ three-fold coincidences with a standard deviation of $\sigma_\mathrm{3-fold}=\langle N_\mathrm{3-fold}\rangle^{1/2}=0.218$. The blue line is a Poisson fit of the overall measurement data, the vast majority of which are time bins containing noise. Our result indicates that we are able to detect time-correlated three-fold coincidences with a signal-to-noise-ratio of $SNR>680$. Assuming for now that the measured rate per time-bin of $33$ three-folds stems solely from noise contributions would mean, that it was $150$ standard deviations away from the average noise-related three-fold coincidence rate per bin. In other words: the probability of measuring a noise-related rate of $33$ three-folds per bin in 