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A very simple Gaussian model is used to illustrate a new fitting result: alinear growth of the resolution with the number N of detecting layers. Thisrule is well beyond the well-known rule proportional to $\sqrt{N}$ for theresolution of the usual fit. The effect is obtained with the appropriate formof the variance for each hit (measurement). The model reconstructs straighttracks with N parallel detecting layers, the track direction is the selectedparameter to test the resolution. The results of the Gaussian model arecompared with realistic simulations of silicon microstrip detectors. Theserealistic simulations suggest an easy method to select the essential weightsfor the fit: the lucky model. Preliminary results of the lucky model show anexcellent reproduction of the linear growth of the resolution, very similar tothat given by realistic simulations.

Beyond the $\sqrt{\mathrm{N}}$ limit of the least squares resolution and the lucky model