varying $\log (n_H)=5-18$ \[\], with one point per decade. The result is plotted at the top panel of Figure \[den\_dist2\] (for wabs1). From the best-fit $n_H\approx 10^{12}$ , $\chi^2\approx 4845$ using 4604 PHA bins (and two free parameters), we compute contour level of confidences 63 %, 90 % and 95 % for $n_H$ and the normalization parameter of the powerlaw.

We remark here, that in an independent way, we are obtaining results that are in agreement with the measurements given by our own method. In the bottom panel we compare these [xspec]{} measurements, i.e., single parameter 90 % confidence level on $n_H$ (vertical dot-dashed line), against the [xline-ID]{} results. The obvious advantage of our method, is that we can track each of the absorption line which is contributing to the COG ratio density diagnostic, overcoming problems like several $\chi^2-$minima, double evaluated best-fit parameter, lack of ability to evaluate what are the spectral ranges contributing to $\chi^2$, among others.

Velocity density distribution {#veldist1 .unnumbered}
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The second main result of the present analysis can be drawn from Figure \[vel\_dist1\]. There we plot the univariate kernel density estimators (k.d.e) for the Doppler velocity distribution: $$\hat{f}_{\rm kern}(v,h)=\frac{1}{nh}\sum_{i=1}^n{K \left( \frac{v-V_i}{h} \right) },$$ where the 