kernel function $K$ is a Gaussian and normalized to unity, i.e., $\int K(x)dx=1$, $h$ is the (constant) bandwidth set to 300 , and $V_1,V_2,...V_n$ is our random sample of velocities of size $n$.

Each possible identification list has the 500 strongest lines for each point of the grid and, as mentioned in section [**Models: The grid**]{}, there are 28 grid points. Whenever a detected feature is out of the identification window ($\Delta \lambda$ Åaround the feature), is flagged as [unknown]{} and is not taken into account in the probability distribution function (p.d.f) analysis. As we may observe from Fig. \[vel\_dist1\], one of the main components have peaks at $v_{\rm out}\approx 300~({\tt wabs1})$ and 420 ([wabs2]{}) , confirming the expected result, as this is what we introduce as outflow velocity in our simulated data (see Table 2).

A closer examination also reveals densities different from zero for higher velocity outflows, for instance exhibits by the LIP Si [IX]{} $\lambda 6.938$ outflowing at $v_{\rm out} \simeq 3600$ , and Si [VIII]{} $\lambda 7.005$ with $v_{\rm out} \simeq 3200$ , which are clearly wrong identifications, consequence of the identification window scheme (for the two plasma, solid line has $\Delta \lambda = 10^{-4}$ Å, 