using the Monte Carlo C++ program $\mathtt{LxJet}$ [@Kotko2013a] implementing (\[eq:HEN\_fact\_2\]). The generated events (weighted or unweighted) are stored in a $\mathtt{ROOT}$ [@Brun:1997pa] file for further processing. For the UGD evolution according to scenarios \[enu:pure-BFKL-equation\]-\[enu:lastmodel\] we solve the corresponding integral equations by a straightforward numerical iteration over a grid over $x$ and $k_T$.

In order to make the fitting feasible, we need a fast routine to calculate $\mathcal{F}$ used in (\[eq:Fit1\]) for the cross section calculation. However, since our numerical procedure is too slow for that, we prepare grids over which we can interpolate the fitting parameters. Each such grid corresponds to a particular parametrization model and arguments range. Out of four parameters ($N$, $A$, $a$, $D$) of the initial conditions, we fix $D=0$ (see Sec. \[sec:Results\]). Moreover, we note that the solution for $\mathcal{F}$ is linear in $N$. Thus the actual grids are in $A$ and $a$.

Results {#sec:Results}
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We have applied the procedure described in the preceding section to most of the models \[enu:pure-BFKL-equation\]-\[enu:BFKLCDS\] and initial conditions \[enu:Models\_ini\]-\[enu:Models\_fini\]. The best values of [$\chi^{2}\!/\mathrm{NDP}$]{}($\chi^2$ per data point) are listed in Table \[tab:chi2\] for models \[enu:pure-BFKL-equation\]-\[enu:lastmodel\]. Note, that some of the scenarios were unable to describe the data, in particular 