should follow a Poisson distribution with the mean $\lambda$ = 0.273. We then calculated the quantity $N_{exp}(k) = P_\lambda(k) \times N_{BS}$ where $P_\lambda(k)$ is the probability mass function of the Poisson distribution representing the background. We give $P_\lambda(k)$ in the second and $N_{exp}(k)$ in the 3rd column of Table \[table:1\] for all k $\leq$ 7. The fourth column in Table \[table:1\] gives the observed number of $k$-neighbourhoods $N_{obs}(k)$ in the BS. Comparing columns 3 and 4 we found that $N_{obs}(k)$ follows $N_{exp}(k)$ quite well for cases $k$=0,1,2. For increasing $k$ the number of observed $k$-neighbourhoods $N_{obs}(k)$ rapidly exceeds the prediction $N_{exp}(k)$. If the case $k$=5 would belong to the background, the expected number of cases would be less than 1, observed are 36. So, we did certainly not underestimate the mean density $\lambda$ for the background distribution. In the case of Praesepe the highest value of $k$ is 405. The ratio $p_{cont}$ = $N_{exp}(k)$/$N_{obs}(k)$ given in column 5 of Table \[table:1\] is the probability that a star in a $k$-neighbourhood belongs to the background distribution. Hence, for each star, we can interpret $p_{cont}$ as the degree how much it is contaminated by background. According to the construction of these $k$-neighbourhoods 