see that the function looks smooth only in the range where the corresponding energy histogram in Fig. 1(a) has enough statistics.

To take full advantage of the histogram reweighting technique we have performed for each $L$ typically three[^3] simulations at slightly different inverse temperatures $\beta_i$. Using histogram reweighting and jackknife-blocking [@r27] we computed the $\beta$-dependence of the expectation values for all interesting thermodynamic observables $O_i \equiv O_L^{(\beta_i)}(\beta)$ plus the associated error $\Delta O_i$. To obtain a single expression $O \equiv O_L(\beta)$ we then combined the values $O_i$ numerically according to the formula $$O = \left( \frac{O_1}{\left( \Delta O_1\right)^2} +
           \frac{O_2}{\left( \Delta O_2\right)^2} +
           \frac{O_3}{\left( \Delta O_3\right)^2} \right)
    \left(\Delta O \right)^2 ,
\label{eq:30}$$ where $\Delta O$ is given by $$\frac{1}{\left( \Delta O \right)^2} =
                                      \frac{1}{\left( \Delta O_1\right)^2} +
                                      \frac{1}{\left( \Delta O_2\right)^2} +
                                      \frac{1}{\left( \Delta O_3\right)^2} .$$ This expression minimizes the relative error $\Delta O/O$. The reweighting range of each simulation, i.e., that range in which the energy histogram has enough statistics to allow for (\[eq:14\]) to be valid, was determined by the energy values at which the histogram had decreased to a third of its maximum value [@alves]. From the energy range one can then deduce a corresponding $\beta$-range. Only the 